{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MC dropout layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MC_dropout(act_vec, p=0.5, mask=True):\n",
    "    return F.dropout(act_vec, p=0.5, training=mask, inplace=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(Linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "        self.pdrop = 0.5\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_dim, n_hid)\n",
    "        self.fc2 = nn.Linear(n_hid, n_hid)\n",
    "        self.fc3 = nn.Linear(n_hid, output_dim)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x, sample=True):\n",
    "        \n",
    "        mask = self.training or sample # if training or sampling, mc dropout will apply random binary mask\n",
    "        # Otherwise, for regular test set evaluation, we can just scale activations\n",
    "\n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x = self.fc1(x)\n",
    "        x = MC_dropout(x, p=self.pdrop, mask=mask)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x = self.fc2(x)\n",
    "        x = MC_dropout(x, p=self.pdrop, mask=mask)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y = self.fc3(x)\n",
    "\n",
    "        return y\n",
    "    \n",
    "    \n",
    "    def sample_predict(self, x, Nsamples):\n",
    "        \n",
    "        # Just copies type from x, initializes new vector\n",
    "        predictions = x.data.new(Nsamples, x.shape[0], self.output_dim)\n",
    "        \n",
    "        for i in range(Nsamples):\n",
    "            \n",
    "            y = self.forward(x, sample=True)\n",
    "            predictions[i] = y\n",
    "            \n",
    "        return predictions\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "class Net(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, batch_size=128, weight_decay=0):\n",
    "        super(Net, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.weight_decay = weight_decay\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = Linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.5, weight_decay=self.weight_decay)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        out = self.model(x)\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "            \n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def sample_eval(self, x, y, Nsamples, logits=True, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model.sample_predict(x, Nsamples)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    def get_weight_samples(self):\n",
    "        weight_vec = []\n",
    "        \n",
    "        state_dict = self.model.state_dict()\n",
    "        \n",
    "        for key in state_dict.keys():\n",
    "            \n",
    "            if 'weight' in key:\n",
    "                weight_mtx = state_dict[key].cpu().data\n",
    "                for weight in weight_mtx.view(-1):\n",
    "                    weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-a863971fdaf1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrainloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         \u001b[0mcost_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0merr_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-3e92c73d765e>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    490\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-ff9174f42aa7>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, sample)\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_dim\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# view(batch_size, input_dim)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMC_dropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdrop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/nn/modules/module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    487\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    490\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/nn/modules/linear.pyc\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mweak_script_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/homes/ja666/anaconda2/lib/python2.7/site-packages/torch/nn/functional.pyc\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m   1350\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1351\u001b[0m         \u001b[0;31m# fused op is marginally faster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1352\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unwrap_optional\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1353\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import copy\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_MC_dropout_MNIST'\n",
    "results_dir = 'results_MC_dropout_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 128\n",
    "nb_epochs = 60 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [5, 10, 20, 30]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "prior_sigma = 10\n",
    "weight_decay = 1/(prior_sigma**2)\n",
    "########################################################################################\n",
    "net = Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size, weight_decay=weight_decay)\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_pred, err = net.fit(x, y)\n",
    "\n",
    "        err_train[i] += err\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "    \n",
    "    # Save state dict\n",
    "    if i in savemodel_its:\n",
    "        save_dicts.append(copy.deepcopy(net.model.state_dict()))\n",
    "\n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "            net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "net.save(models_dir+'/theta_last.dat')\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " \n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36mReading models_MC_dropout_MNIST/theta_last.dat\n",
      "\u001b[0m\n",
      "  restoring epoch: 59, lr: 0.001000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import copy\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_MC_dropout_MNIST'\n",
    "results_dir = 'results_MC_dropout_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 128\n",
    "nb_epochs = 60 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "savemodel_its = [5, 10, 20, 30]\n",
    "save_dicts = []\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-3\n",
    "prior_sigma = 10\n",
    "weight_decay = 1/(prior_sigma**2)\n",
    "########################################################################################\n",
    "net = Net(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, batch_size=batch_size, weight_decay=weight_decay)\n",
    "\n",
    "net.load(models_dir+'/theta_last.dat')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -435.458069, err = 0.013700\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 200\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, logits=False) # , logits=True\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian [IGNORE THIS FOR NOW]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 100\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All dataset with entropy\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 100\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples)\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    line_ax = ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)\n",
    "    \n",
    "    return line_ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "line_ax0 = errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "line_ax1 = errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lns = line_ax0+line_ax1\n",
    "\n",
    "\n",
    "lgd = plt.legend(lns, ['correct class', 'predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.75,1))\n",
    "\n",
    "\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight histogram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2392800,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'MC Dropout'\n",
    "# mkdir('weight_samples')\n",
    "weight_vector = net.get_weight_samples()\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=120)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(weight_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d' % len(weight_vector))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution over iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vLM4FalF4KiurZBiGuro6FI/H8t0cjMI0TfX2dskwDFVVVdr2vrZU5rxer4LBoD7ykY8Mef7ggw/WO97xDj377LPaunWrDj106gOGi3V1b6n4VyjHIK5l/rjdTm1Jh7nGugqFwiP/wxeLJRSLJTUQHH38bsDvkSkd8DhJilR61d8f1ZELBrdU+tOzO3Tmu9ivtRAU+8+kaRry+SoVDPaqra1FDodLhmFomidpFyRru75CKwKZppRMJmWaCbndPsVizjH/vhXdDhCzZ6f2O5s5c+aw1xoaGiRJPT2jb6kDAOO1pzOovmAqwM2e5rXlRjK/sVINtakJF4ybQ66kKj21qqtrlM8XkMPhKMsgJ0kul1MulzPfzRjGMCSn06nKyhrV1c0cskf0dLOlMnfsscdqy5Yt2r17txYvHrpB7e7duyWNHPQAYKJefXtwmaR8hDnDMHT84kY9unGH3mrpUU9/RDWVLMeEqTMMQ15vhbzeinw3Ja+Kvco6HWypzF144YWSpDVr1gwpi7766qvatGmTDjnkkAktdQIAo3n17X2SJJ/Hqdo8hajjDk/1OJiSXnizIy9tAFA+Jl2ZW79+vXbt2iVJam1tlWmaWrlyZeb1q666KvP4Xe96ly666CKtXbtWn/70p3X66aero6MjMwHixhtvnMJHAICUpGnqtW1dkqSmer+t3RzZDplTreqAR70DUW3e0aUPvGNuXtoBoDxMOsytW7dOGzduHPLcrbfemnmcHeYk6Tvf+Y4WL16stWvXasWKFfJ6vTr++ON19dVXa+nSpZNtBgBk7GzrV38oNV5uuvdiHYvDMHTonGq9+GZHZn9YAJgukw5zq1evntDxTqdTl19+uS6//PLJviUAjOm17V2Zx/kYL5ftkHSY6+gJq3cgqurA2OvUAcBkFeaKewAwCc3bU5MfagIeVVbkd2HVhbMH94PN3icWAHKNMAegJMQTSb2xs1uSdPDs6ryNl7MsnF0tqwVv09UKYBrZsjQJAEy3rbt6FY0lJdm7H6ulpsorl8sh67bq8bg0pyGg1r0D2ranTx7P4O3WNE3FYgnb2wigNBHmAJSE5m2D68sdNLta3X1hW9/f6TC0eUe3ItF45rkZ1T617h3Qmy3devXtfTIMQ263Q4fNrR3jTAAwMYQ5ACXh9fTkh3kNAVVWuG0Pc5IUiyfV0t6X+XOFN3WLDUcTem1bp6oDHs1rtL9qCKC0MWYOQNEzTVPb2/olSYcvqMtzawbNrPFlHu/tDuWxJQBKGWEOQNHrGYgqkh6DNntmIM+tGVRX5ZXTkZoG0dFjf6UQQHkgzAEoem2dg3s0NtXnd325bA6HofrqVHWOMAdguhDmABS9tq7BLsymGYVTmZMGu1q7esNKJJJ5bg2AUkSYA1D0rMqc02GoodZ3gKPtNTPdnqQpdfZF8twaAKWIMAeg6FmVuZk1PjkdhXVby54E0dFNVyuA3Cusux4ATEJbV6oyN6uAxstZKivc8nmckqSOHma0Asg9whyAopY0TbWnK3Oz6govzBmGkanOMQkCwHQgzAEoal29EcXiqYkFs+or8tyakVlhri8YUzAcP8DRADAxhDkARc3qYpUKszInSTNrB0Pm7n0DeWwJgFJEmANQ1LKXJSnUytyMrEkQuzoIcwByizAHoKhZy5K4nI7MAr2Fxut2KuBL7dO6j3FzAHKMMAegqFlhrrGuQg7DyHNrRlcd8EiSOvsIcwByizAHoKi1ZWayFmYXq6XKnwpzXb0RmaaZ59YAKCWEOQBFK5FMam93OswV4Bpz2ar9bklSJJZQXzCW59YAKCWEOQBFa19vRIlkqspV8JW5dDerJO3pDI5xJABMDGEOQNFq6yz8ZUksVmVOGtpuAJgqwhyAojUkzBV4N2ul3y1regaVOQC5RJgDULSsyQ8et0O1lZ4DHJ1fTodDgYpUdY7KHIBcIswBKFrW7g+z6vwyCnhZEkuVnzAHIPcIcwCKlhWKCn3yg8VanmRPZ5DlSQDkDGEOQFGKJ5LqSO+mUOjj5SzVgVRlLhSJqy/E8iQAcsM12S9ctWqVmpub1dzcrB07dsjhcKi5uXlcX9vW1qZzzz1XfX19+sIXvqCrrrpqss0AUCbcbueQrtSOjgFZxa25DZXyeFK3M5fLIYejMLtcrcqcJLV3hlTtL+xxfgCKw6TD3C233KLq6motWbJEwWBQnZ2d4/7ab33rW0okEpN9awBlyDAMvdnarVgsKUl6s6U781ooGlfzttQ9qLHeL6kww9yQ5Um6glo0ryaPrQFQKibdzfrEE0/o2Wef1f/93/9p4cKF4/66hx9+WE899ZSuueaayb41gDIViyXV0t6nlvY+vd3ak3k+HIlnno8nknls4dgq/W5ZxUVrJi4ATNWkw9yCBQsm/DWdnZ266aabdNlll+moo46a7FsDgHqDUUmS2+WQz+PMc2vGx+lwqDq9E0R7FzNaAeTGpLtZJ+N73/ueKioqdO211+rll1+elveoK/BV4MfidKaydTF/BqRwLXPPMCSv16VAepzZQDguSaqt9Koy4M0c53Y75XY7MseNZjzHGQ5DhpSz80nSzJoK9fRH1dEb4e+HjfiZLB1cy+Fsm8361FNP6fe//71uvPFG+f1cAABT0zuQqszVFPhiwfurr/ZJkvZ0DLA8CYCcsKUy19/fr29+85s677zzdOqpp07re3UVcdeF9VtGMX8GpHAtc8/jcSkSiWsg3b0aTFfmPC5H5jlJisUSisWSQ54byXiOC/g9MqWcnU+SatLLkwQjce1o7cl0u2J68TNZOkrlWjY0VOXsXLZU5n7wgx8oHA7rq1/9qh1vB6DExeJJJZKpqpa3SMbLWerSlTlpcAcLAJiKaQ9zr776qu699159/OMfV39/v7Zv367t27erra1NktTT06Pt27erv79/upsCoEREYoNLGxXL5AdLXdXg+L52ZrQCyIFp72bdvXu3TNPUypUrtXLlymGv/+pXv9KvfvUr3XTTTbr44ounuzkASkA4mh3mbJ3HNWW1lV4ZhmSaVOYA5Ma03wWPOeYY3XrrrcOef+utt/S///u/Ovfcc3XmmWfq6KOPnu6mACgRkWg889jrLq7KnMvp0Mwan/Z2h6nMAciJSYe59evXa9euXZKk1tbWTPXNYm3RNWvWLJ199tnDvv6ZZ56RJC1atGjE1wFgNEMrc8UV5iRpVn1Ae7vDauskzAGYukmHuXXr1mnjxo1DnsuuwLHfKoDpUuxhrqner1e27lNbV1CmaQ7ZcxYAJmrSYW716tVTeuN3v/vd2rx585TOAaA8WWHOYaR2gCg2s+pTSyuEown1BWMsTwJgSorvLgig7EXSYc7rcRZlVaupfnDhdCZBAJgqwhyAohNOT4AotpmsllnZYY5xcwCmiDAHoOhY68wV20xWS2NdhayCYns3lTkAU0OYA1B0rDFzxTj5QUotT1JbmVo8uLM3kufWACh2hDkARSecNWauWFk7QXT1EeYATA1hDkBRSSRNxeJJScVbmZMIcwByhzAHoKhEinyNOUtdupu1q58wB2BqCHMAiko4ayuvYp3NKg1W5iLRhEKR+AGOBoDREeYAFJXs3R+KecxcbTrMSVInXa0ApoAwB6CoWMuSSJKvSJcmkaT6rDDXTZgDMAWEOQBFpRQrc0yCADAVhDkARSVSImHOmgAhMQkCwNQQ5gAUFWsChNftlKMI92W1eNxOBXypCRxU5gBMBWEOQFEp9t0fslkzWhkzB2AqCHMAikqkBHZ/sNSycDCAHCDMASgqJVWZsxYO7gvnuSUAihlhDkBRyezLWsTLklisbtbeYEzxRDLPrQFQrAhzAIpGMmkqGiuhylz2WnPMaAUwSYQ5AEUjHI3LTD8u5q28LEPCXF80jy0BUMwIcwCKRjA8uIdpSUyAqMze0otxcwAmhzAHoGgEszakL4Vu1vpqX+Yxy5MAmCzCHICikV2ZK4UwF/C55HKmbsPsAgFgsghzAIpGMBzLPC6FMGcYhuqqPJJYaw7A5BHmABSN7G7WUhgzJ2WvNUeYAzA5hDkARcPqZnU7HXI6SuP2VZceN0eYAzBZpXE3BFAWrDBXKlU5abAy190fkWmaBzgaAIab9EJNq1atUnNzs5qbm7Vjxw45HA41NzePeOzGjRv12GOP6dlnn9WuXbtkGIbmz5+vD3/4w/rYxz4mn8834tcBQLZgJDVmrhTGy1ms/VnjCVN9oZiq/Z48twhAsZl0mLvllltUXV2tJUuWKBgMqrOzc9Rjb775ZrW2tur000/Xxz72McXjcW3YsEErVqzQQw89pDVr1sjr9Y769QAgDVbmSinM1Q9ZODhCmAMwYZMOc0888YQWLFggSVq+fPmYYe6LX/yijj/+eLnd7sxzy5cv15e+9CX9/ve/19q1a3XppZdOtikAykQpdrPWZoW5rr6IFsyqymNrABSjSY+Zs4LceJx44olDgpzlnHPOkSRt3rx5ss0AUCZM08zMZi2FrbwsdVm7QLDWHIDJyOsdsa2tTZI0Y8aMnJ2zrs6fs3PZzZlePLSYPwNSuJa5FwzHlEymJghUBTwKjNId6XY75XY7Rn19IscZDkOGlLPzSZLX61JlpUemmTquqtonw5BMUwrFkvydmSb8TJYOruVweZvN2t/frzvuuENut1vnn39+vpoBoEj0DgxuRF/hLd7KXFUgFeIMI/Wf2+XI7NHa2RvOPG/9BwAHkpc7Yjwe1/XXX6/W1lZ95Stf0cKFC3N27q6uYM7OZTfrt4xi/gxI4VrmXlvHQOaxYZoaCEZHPC4WSygWS476+kSOC/g9MqWcnU+SkomkXnqjQ5Ho8AWQ327t0cZX9khKVQMPm1uraNZxmDx+JktHqVzLhobcjY+1PczF43F96Utf0p///GddccUV+uQnP2l3EwAUoezKXLHPZo3Fk2pp78v82dqftbM3nHl+XiMTIQCMj61hLhaL6Utf+pIee+wxffazn9UXv/hFO98eQBHrCw7uy1pKs1klyZ/uNrZm6wLARNgW5qLRqK677jpt2LBBn//853XNNdfY9dYASkBvMLsyV7xj5kYS8KU+TzSeVDyRzFTqAGA8bLljRKNRXXvttdqwYYOuv/56ghyACetLd7M6HYZcztKaGeD3DYZTqnMAJmrSv96uX79eu3btkiS1trbKNE2tXLky8/pVV12VeXzDDTfoT3/6k4477jjNnj1bDzzwwJBzLViwQO94xzsm2xQAZcCqzHk9ThklNs0ze3ZuMBxXdYBdIACM36TD3Lp167Rx48Yhz916662Zx9lh7pVXXpEkvfDCC3rhhReGnevCCy8kzAEYkzVmrtgnP4wkkF2Zi8TGOBIAhpt0mFu9evW4j33yyScn+zYAIEnqsypz7tILc37f4A45dLMCmChG2QIoCqVcmXO7HJlxgNaWZQAwXoQ5AEUhlA45nhKszEmD4+bCkUSeWwKg2BDmABQ80zQzYc7tKs3bllVxDEcJcwAmpjTvigBKSiyeVCJpSirlMJeuzLF9F4AJKs27IoCSEsqqVpVumKMyB2BySvOuCKCkhLImBXhKNcxZY+aiCSVNM8+tAVBMSvOuCKCkZIc5t6s0J0Bkz9KNUJ0DMAGEOQAFL5wd5kp039KKrDBHVyuAiSjNuyKAklIeY+YG13BnEgSAiSjNuyKAkjK0m7U0b1s+b1ZljrXmAExAad4VAZSUcFlU5uhmBTA5pXlXBFBSymE2q9ftlJHa0YtuVgATUpp3RQAlJZQON06HIWeJToAwDCNTnQtRmQMwAaV5VwRQUqwxZKW6L6slswtEhMocgPEjzAEoeFZlzlvyYY5dIABMHGEOQMGzKnNed2nfsghzACajtO+MAEqCNQHC4yn1ypy1pRfdrADGjzAHoOCVTTdreq25eMJUNEZ1DsD4EOYAFLzBbtbSDnMVWbtABMNU5wCMD2EOQMGzKnOlP5t18PMNhGN5bAmAYkKYA1DwrDFzpV6Zy97Sa4DKHIBxIswBKGixeFLxhCmpHGazZnezUpkDMD6lfWcEUPRCWTM7y6ublcocgPEhzAEoaNm7IXhLfGkSl9Mhd3q7MipzAMaLMAegoIUig0t0lPqYOWlw3NxAiMocgPEhzAEoaNkL6JaOqqZkAAAgAElEQVRFmEtXH5nNCmC8XAc+ZGSrVq1Sc3OzmpubtWPHDjkcDjU3N496fDwe1y9+8QutW7dOra2tqq2t1emnn67rrrtOdXV1k20GgBK3f2UukUzmsTXTz5oEwTpzAMZr0mHulltuUXV1tZYsWaJgMKjOzs4xj//KV76iBx98UB/4wAf06U9/Wq2trbrrrrv03HPP6be//a0qKysn2xQAJWzIBAiPU6FwqYc5KnMAJmbSYe6JJ57QggULJEnLly8fM8z9/e9/14MPPqjTTjtNP/vZzzLPH3PMMbr66qt155136gtf+MJkmwKghA2ZAOF2KlTiIcfnTd2WQ5G4kkkzz60BUAwmPWbOCnLj8cADD0iSPvWpTw15/owzztBBBx2UeR0A9heKZnezlv4wX6syZ5pSXyia59YAKAa23BlfeuklORwOLVu2bNhry5YtU2trq/bt22dHUwAUGWv3B4dhyOUs/TBXkbX8Su8AYQ7AgU26m3Ui9uzZo7q6Onk8nmGvNTU1ZY6ZMWPGlN+rrs4/5XPkizP9D1UxfwakcC1zxzQMSZLf55LP51bAP/w+ks3tdsrtduTkOMNhyJBydr7xHFdb7cs8jiVN/g7lCD+TpYNrOZwtv+aGw+ERg5ykzPOhUMiOpgAoMtaszgqfLb975l2Fd/BzdvdF8tgSAMXClrujz+dTNDpyd4H1fEVFRU7eq6srmJPz5IP1W0YxfwakcC1zp6cvLEnyuZ2KROIaCI7d9RiLJRSLJXNyXMDvkSnl7HzjOc7MWnqlvTPI36Ec4WeydJTKtWxoqMrZuWypzDU1Namrq2vEQLdnz57MMQCwP2vMXHbFqpR53U6le5YZMwdgXGwJc0uXLlUymdRLL7007LVNmzZp7ty5ORkvB6D0WLNZyyXMGYaRmdFKmAMwHraEuQsuuECS9Itf/GLI8xs2bND27dt1/vnn29EMAEUoXGaVOWlwF4gewhyAcZj03XH9+vXatWuXJKm1tVWmaWrlypWZ16+66qrM4/e+970677zz9Pvf/17/8R//odNPP10tLS266667dMghh+iKK66YwkcAUMoGK3Olvy+rhcocgImYdJhbt26dNm7cOOS5W2+9NfM4O8xJ0ooVK7R48WLdf//9+va3v63a2lqdf/75uu6669jKC8CoMpU5TzlV5ghzAMZv0nfH1atXT+h4t9utz372s/rsZz872bcEUGbiiaSi8dTsznJZmkQa7GYlzAEYj9JfTh1A0QpnbeVVVmPm0l3KkVhCkazvAQCMhDAHoGBZXaxSmYW5rC7l3gOsWwcAhDkABStUppW5IfuzEuYAHABhDkDBCmVX5sppAkTWzN2+gVgeWwKgGBDmABSscDS7m7WcliahmxXA+BHmABSsUKQ8u1l92d2szGgFcACEOQAFKxQtzwkQLqdDHnfq9swuEAAOhDAHoGCFy7QyJ0mVFR5JUk9/JM8tAVDoCHMAClYwPQHCkOT1lM+YOUmq8rslSd39VOYAjI0wB6BgWevM+bxOOQwjz62xV2WFFeaozAEYG2EOQMGyxsz5ymhZEstgZS4i0zTz3BoAhYwwB6BgWWPmym28nDQ4Zi6eMDUQjh/gaADljDAHoGBZlblyWmPOYnWzSlJ3H12tAEZHmANQsKx15spp9wdLpT8rzDFuDsAYCHMACpa1A4SvDLtZq4aEOWa0AhgdYQ5AwbL2Zq0os2VJpP26WanMARgDYQ5AwQpFy3cChNvllN+X+tyEOQBjIcwBKEjJpKlIOsz5yrAyJ0l1VV5JdLMCGBthDkBBCkfLdysvS12lFeaozAEYHWEOQEGyJj9I5RvmaqsIcwAOjDAHoCBZkx+k8u1mrU1X5nr6o0qyCwSAURDmABSkEN2smTFziaSp/mAsz60BUKgIcwAKUnZlrhwXDZYGw5xEVyuA0RHmABSkId2sZbidlyTVVvkyj5nRCmA0hDkABWnIbFYqc1TmAIyKMAegIA3pZi3XylwlYQ7Agdn2625/f7/uuusuPfroo2ppaZHH49G8efP0kY98RJdcconcbveBTwKgbAydzVqelTm3y6HKCrf6QzG6WQGMypY7ZDwe1+WXX67m5mZdcMEFuvTSSxWJRPToo4/qO9/5jl588UXdfPPNdjQFQJGwulm9HqccDiPPrcmf2kpPKsz1UZkDMDJbulk3btyoV155RZdffrlWrFihf/u3f9Pll1+uX//61zriiCP08MMPq7+/346mACgSVmWuokzXmLPUsAsEgAOwJcz19fVJkmbNmjXkeafTqYaGBjmdTnk8HjuaAqBIWOvMlesac5baytS9kTAHYDS23CWPO+44VVRUaNWqVZo1a5aWLVumSCSiRx55RH/961917bXXEuYADBFOV+bKdbycJbMLxEBUyaRZ1l3OAEZmy12yoaFBP/nJT/Ttb39b119/feZ5r9erm266SRdddFHO3quuzp+zc9nN6UwVSov5MyCFazl1sURq+6rqgEd1dX4ZhuT1uhTwj/2Ln9vtlNvtyMlxhsOQIeXsfBM5zut1qbLSozmNVZIk05QcHqfqstaew/jxM1k6uJbD2fYrb01NjQ499FCdeOKJOumkkxQOh/XAAw/om9/8piTlNNABKH5Ba8ycr7wrc/XVg8uTdPZGCHMAhrHlLvn666/r4x//uC6//HLdcMMNmeetma3f/e539f73v18zZ86c8nt1dQWnfI58sX7LKObPgBSu5dT1B1NLcTiN1PfR43EpEolrIDj2Eh2xWEKxWDInxwX8HplSzs43keMilV7190flyupV3bGrR/V+lnGaDH4mS0epXMuGhqqcncuWCRB33XWXotGozj777CHPG4ahs846S+FwWJs2bbKjKQCKxEA4tbF8wFfe4aWOhYMBHIAtYa69vV2SlEwmh70Wj6e6UhKJxLDXAJSneCKpaCx1v/CXeTdrdWBwbB1rzQEYiS1hbtGiRZKk+++/f8jzsVhMDz74oBwOh4455hg7mgKgCAyEB3d/KPfKnMvpUFW6a5VdIACMxJZfeS+//HI98MADWrNmjfbs2aNTTjlFoVBIDz74oDZv3qzly5drzpw5djQFQBEIprtYJSlQ5pU5KbU8SV8wRjcrgBHZcpecM2eO1q5dq5UrV+pvf/ub/vKXv8jtdmvRokX67ne/q4svvtiOZgAoEtmVOX+ZV+akVJjb2d5PmAMwItt+5Z03b56+//3v2/V2AIrYQIjKXDZrF4geulkBjMCWMXMAMBHBIZU5wpy1C0TvQFSJESaSAShvhDkABWcge8xcBd2stVWpMGdK6h2IjX0wgLJDmANQcIZU5rxU5qxuVom15gAMR5gDUHCsCRBej1MuJ7ep2uyFg1lrDsB+uEsCKDiDuz9QlZP2C3NU5gDshzslgLxyu50yDGPIc6FoakeYygq3PJ7UbcrlcsjhMIZ9fTmoDrhlGJJpSl3MaAWwH8IcgLwyDENvtnYrFhucpbm3O5R53LytU5LUWO+XVJ5hzulwqMrvUe9AVD1U5gDshzAHIO9isaRa2vsyf+4PDlafrOfra3y2t6uQ1AbSYW6AyhyAoRgzB6DgRNJVOo/LmeeWFI6a9Lg5xswB2B9hDkDBicZSY+Y8bm5Rlhp2gQAwCu6UAApKIpFUImlKkjxuKnMWa6253mBUyfT3BwAkwhyAAhPJmghBZW5QTSC9C4SZCnQAYOFOCaCgROOJzGMvlbmM7F0g6GoFkI0wB6CgWOPlJCZAZKth4WAAoyDMASgo0axuVi/drBm1gazKHMuTAMjCOnMACkokuzJXxt2sNVVeuVwOWbfpmfX+zGt9oVhmZwxJMk1TsazvG4DyQpgDUFCiTICQJDkdhjbv6FYkGs885/M4FY4m9Pau3szOGG63Q4fNrc1XMwEUAMIcgIISYcxcRiw+dGcMrzsV5jp6Qpnn5zVW5at5AApE+f7aC6AgWbNZ3S6HHI7y3It1NBXe1O/fwXD8AEcCKCeEOQAFJZrZyovb0/4qvKlKZShCmAMwiLslgIISyWzlVd5drCOxKnOhSEKmyS4QAFIIcwAKirXOHAsGD+dPh7mkaQ6ZKAKgvBHmABSUTDdrGc9kHY1VmZPoagUwiLslgIJiTYCgm3W47DAXJMwBSCPMASgYpmkqkq7MsfvDcFTmAIyEuyWAgpFImkomUwP7y32NuZFU+Aa/J4Q5ABbCHICCEWUrrzG5nQ65nKm190IRtu8CkGLrDhD9/f26/fbb9fjjj6u1tVU+n08HHXSQPvGJT+iCCy6wsykAClCErbzGZBiGKrwu9QVjVOYAZNgW5tra2nTZZZepq6tLF154oRYtWqRQKKRt27Zp165ddjUDQAHLrsyxNMnICHMA9mdbmPvyl7+sgYEBPfDAA5o9e7ZdbwugiETj2ZU5wtxIKjyp7wuzWQFYbOnHeP755/WPf/xDV1xxhWbPnq1EIqGBgQE73hpAEYlEsytzdLOOZHAXCMIcgBRbKnNPPfWUJGnBggW65ppr9Kc//UmxWEyNjY269NJLdeWVV8rhyM2Nu67On5Pz5IPTmfoeFPNnQArXcvwMQ/J6XQr4Pak/pNVW++TzDN6i3G6n3G5H6rgx5PI4w2HIkGx/37GOq6nySpLiCVMej1Ner0uVlR6Z5tjnK3f8TJYOruVwtoS5LVu2SJK+9rWvacGCBbrpppskSffcc49+/OMfa9euXfrOd75jR1MAFLAIs1kPyO9zZx4PhKnOAbApzFldqoFAQKtXr5bHk/oN8txzz9W5556re++9V5/85Cd1yCGHTPm9urqCUz5Hvli/ZRTzZ0AK13L8PB6XIpG4BoJR9QejkiS3y6FQKDbkuFgsoVgsqYH0MaPJ5XEBv0emZPv7jnVcdh9GZ3dIkaa4+vujikYJdmPhZ7J0lMq1bGioytm5bBmU4vP5JKXCmxXkJMntduu8886TaZp65pln7GgKgAJmzWZlJuvo/CwcDGA/toS5pqYmSVJDQ8Ow16znenp67GgKgAJmzWZljbnRDd3Si4WDAdgU5pYtWyZJ2r1797DXrOdmzJhhR1MAFDBrNivj5UbndTsz80RYngSAZFOYO/3001VdXa0HHnhA/f39mecHBga0fv16ud1unXzyyXY0BUABsypzXheVudFYu0BIdLMCSLFlAkRVVZW+9rWv6T//8z910UUX6eKLL5YkrVu3Tm1tbbr++utZSBhAZswclbmxVXhdCobjhDkAkmzcAeJf/uVfVFdXp9tvv10/+clPlEwmtXjxYv3oRz/Sueeea1czABQo0zQJc+NEZQ5ANtvCnCSdeuqpOvXUU+18SwBFIp4wlTRTj9n9YWx+byrsMgECgGTTmDkAOJAoCwaPm1WZi8QSSiSSBzgaQKkjzAEoCNE4YW68spcn6d9vcWUA5YcwB6AgRGKDFSYPs1nHRJgDkI07JoCCkN3Nyg4QY6vwDn5/CHMACHMACkI0uzLHBIgxUZkDkI07JoCCEKEyN24VHsIcgEGEOQAFIbub1c2YuTE5HIZ8nlTgHQgS5oByxx0TQEGwtvLyuB0yrM1HMSqrq7U/TJgDyh1hDkBBsLpZPS66WMfDmgRBNysAwhyAgmBNgGC83PhkKnN0swJljzAHoCAM7svKbWk8rDA3EI4pae2DBqAscdcEUBAy3axU5sbFCnOmKfUFo3luDYB8IswByDvTNBWKxCUNXRAXo/NnrTXX3R/JY0sA5BthDkDehaMJxROprsKAz53n1hSH7NDb3UeYA8oZYQ5A3vUODHYTBnyuMY6EpWJIZY5uVqCcEeYA5F1vMDvMUZkbjwq6WQGkEeYA5F12Zc5fQWVuPFxOR2anDMIcUN4IcwDyri8d5gxjaMUJY7O+V4yZA8obYQ5A3vWkw1yF1yUHW3mNmzUJgsocUN4IcwDyzhozx+SHiclU5ghzQFkjzAHIO6ublckPE+PPdLNGZZrsAgGUK8IcgLxKmqZ60/uLBpj8MCFWZS6WSCqYXnQZQPkhzAHIq96BaGZvUT+VuQlhrTkAEmEOQJ7t6wlnHjNmbmKyd4HoZdwcULYIcwDyqrM3O8xRmZuIIZW5ASpzQLkizAHIq+zKnJ/K3IT4s8JcD92sQNnKW5hLJpO65JJLdPjhh+uTn/xkvpoBIM/2pStzDochn8d5gKORze1yyOVMrcvH8iRA+cpbmLvrrrv05ptv5uvtARQIK8wFfC4ZLBg8IYZhqLIi1TXdQzcrULbyEuZ27typW2+9Vdddd10+3h5AAensscIc4+UmIxPmqMwBZSsvYe7rX/+6Fi1apOXLl+fj7QEUEKsyx3i5yQmkwxxLkwDly/a757333qvnnntO69atk8PB/AugnCWSycxYL5YlmZzBblYqc0C5svXu2dbWph/84Af61Kc+pSOOOGJa3qOuzj8t57WD05kKt8X8GZDCtRyfvV0hWbtQ1VX7FPB7Rj3W7XbK7XaMeUyujzMchgzJ9vedyHE1VT5JUiiSkD/glZdJJCPiZ7J0cC2Hs7U09u1vf1u1tbX6/Oc/b+fbAihQe7tDmceVfsbMTUZV1vetsy88xpEASpVtlbmHH35YGzZs0C9/+Uv5fL5pe5+uruC0nXu6Wb9lFPNnQArXcny27+rOPHYahgaCo4/7isUSisWSYx6T6+MCfo9Myfb3nchxPvdgJW5Ha498DmYEj4SfydJRKteyoaEqZ+eyJcxFo1HddNNNOvnkkzV37lxt3759yOvhcFjbt29XIBDQzJkz7WgSgALQ1Ts4zosxc5NjjZmTWJ4EKFe23D3D4bA6Ozv117/+VWeeeeaw11988UWdeeaZ+vCHP6ybb77ZjiYBKACd6TDncTvkcTPWazICWWGOhYOB8mRLmKuoqNCtt9464mtf+MIXtHjxYl199dWaM2eOHc0BUCCsMV7VBxjkj9EFfC45DENJ02RLL6BM2RLm3G63zj777FFfnzFjxpivAyhNVmWuOkCYmyzDMFRT6VFXX4SFg4EyxUJvAPKGylxu1FZ6JTFmDihXeR9xvHnz5nw3AUAeRGMJ9QVjkqTqSsLcVNSmv3/sAgGUJypzAPKiq2+wS5DK3NTUVlmVObpZgXJEmAOQF529gwvcVjFmbkqsbta+YEzxRDLPrQFgN8IcgLzozK7MEeamxApzktTLuDmg7BDmAORFdmWumq28pqQmK8wxCQIoP4Q5AHlhVeYqK9xyu1gweCpqsyaQsHAwUH4IcwDyYl+6MjejZvr2ai4X1gQISSwcDJQhwhyAvLAWDJ5RTZibquwxc1TmgPJDmANgu1g8qbbOoCSpaYY/z60pfi6nQ3Xp6lx7VyjPrQFgN8IcANu1dvQrkTQlSQfPrs5za0pDU30qFO9Oh2QA5YMwB8B22/f0ZR4f3ESYywWrwrmnMyjTNPPcGgB2IswBsN32tn5JktfjpJs1R6zKXCSaYFsvoMwQ5gDYzqrMLWislMMw8tya0jA7KxTv2TeQx5YAsBthDoCt4omkdranKnMHzarKc2tKh1WZkxg3B5QbwhwAW+3ZF8zsH3pQE2EuV+qrffK4Urf0PfsIc0A5IcwBsNX2tsHJD1TmcsdhGJrFjFagLBHmANjKCnNul0OzZzL5IZesrlYqc0B5IcwBsNWO9OSHeQ0BOR3cgnLJmgTR2RtWJJbIc2sA2IU7KQDbJE1T25n8MG2sypwpZXbYAFD6CHMAbNPeFVIkmqoYLWDyQ87NnhHIPN5DmAPKBmEOgG2yd36gMpd7s+orMo8ZNweUD1e+GwCgNLndThn7LQjcsje1mK3TYWjh3Bp5XE65XA45HCwcnAs+j0t1VV519UWozAFlhDAHYFoYhqE3W7sViyUzz7369j5J0owan95q6ZEkNdb7JRHmcqWp3q+uvgjLkwBlhDAHYNrEYkm1tKe6Vk3T1K70NlNVfnfm+foaX97aV4qaZvj12vYu7ekMyjTNYdVRAKWHMXMAbDEQjiuartLNqCbATZfZ6RmtkWhC3f3RPLcGgB2ozAGwRWdvOPO4njCXMzVVXrlcDlm38/lZE0v29oQ1K2uGq2mairH+HFByCHMAbLGvN5J5XFflzWNLSovTYWjzjm5FonFJUn8olnntxTf2ZiaXuN0OHTa3Ni9tBDC9bAlz27Zt00MPPaSnn35aO3fu1MDAgObMmaP3vve9uvLKK9XY2GhHMwDkkVWZqwl45HYxwiOXYvGhYxOdDkOJpKntbb2Z5UrmNbIUDFCqbAlza9eu1erVq3Xqqafq7LPPVkVFhTZt2qTf/OY3euihh/Sb3/xGhx56qB1NAZAHyaSpvV0hSVJ9NVW56WQYhqoDHnX1RdTDmDmgLNgS5s466yx95jOfUU1NTea5j370o1q2bJm++c1v6n/+539066232tEUAHmwtzukaDw1+WHOzMABjsZU1aTDXO8AYQ4oB7b0dRxzzDFDgpzl3HPPlSRt3rzZjmYAyJOWvf2Zx3MbCHPTrTrgkZSaQRxPJA9wNIBil9eBK21tbZKkGTNm5LMZAKZZS3tqfbmGWp98HuZdTbeadJiTRHUOKAN5vav+93//tyTpX//1X3N2zro6f87OZTenM5Wti/kzIIVrKRmG5PW6FE+a6kkHikPm1ijg9ww5zu12yu12DHt+f/k4znAYMqSCbd9ox2UvRxKJJxXwe+T1ulRZ6ZFpjn2+UsXPZOngWg6Xt8rcz3/+cz3++OM644wzdOGFF+arGQCm2fY9fZnHBzVV57El5aO2ajCwdfaExzgSQCnIS2Xurrvu0o9//GO9613v0s0335zT7Wa6uop3P0Lrt4xi/gxI4VpKHo9LkUhcW1tTe7AGfC55XYYGgkO7/WKxhGKx5LDn95eP4wJ+j0ypYNs31nF1VV519UW0bXefjlpYr0ilV/39UUXT69GVG34mS0epXMuGhtwtF2R7Ze6Xv/ylvv/97+s973mPVq1apYqKCrubAMAmkVhCbekN3+c1VrJPqI3mpSea7OsNKxguzwAHlAtbw9ztt9+uFStW6JRTTtFtt91GkANK3Nu7epU0U4/nMYvVVvMaKzOPWzv6xzgSQLGzLczddtttuvnmm/WBD3xAK1eulNfLwqFAqXurpVuS5HIaaqpnsLKdZtT45PM4JQ3OJgZQmmwZM3f33XfrRz/6kWbOnKkPfvCDeuSRR4a8HggEdMYZZ9jRFAA2SZqm3kqPl2uaEcjMQIM9HIahuTMD2rKrV7v3DbDeHFDCbAlzL7/8siSpo6NDX/3qV4e9PnfuXMIcUGLe3tWbGatFF2t+zGus1JZdvYonTG3f06elh87Md5MATANbwtyKFSu0YsUKO94KQIF48Y29mceEufyYPdMvw5BMc7DLG0Dpod8DQM6ZpqnnXk/t8FJf7ZXf585zi8qTx+XUrPRYxTdbemSaZp5bBGA6EOYA5NzrO7rVsjc16P6gWblbSwkTN78hNau1dyCqlnZmtQKliDAHIOf++NxOSalZrIfNr8lza8rbvMbBLu4X39w7xpEAihVhDkBOtXcFtenNDknS0QtnyOfJ6xbQZa/K71FNILW914tvdOS5NQCmA2EOQE5teL5V1sisE5Y05rUtSJmbnoDyVku3eg+wPRiA4kOYA5AzoUhcf/nnLknSkQfXq7GOhYILgbUbhCnppbeozgGlhjAHIGf++vJuhaMJSdLZ716Q59bA0lhboQpvqrv78Wd3KsmsVqCkEOYA5ETSNLXh+RZJqfCwbHFDnlsEi8Nh6N1HzpIkte4d0AubmQgBlBLCHICc+OeWfWrvCkmSTj9+nhyGkecWIdvxRzSqsiK13t8DT79NdQ4oIYQ5AFOWTJp6+G/bJEk+j1MnL52d3wZhGK/bqXPec7CkVHXueapzQMkgzAGYELfbKY/HNeS/PzyzQ1t29UqSTjt+nmqqfHK5HHI4qM4VkjPfNT9TnXvwr1TngFJBmAMwIYZh6M3WbjVv61Tztk49+sx2/e7PWyRJM2p8OmphvZq3dWpPZ1ASYa6Q+DyuzMSU1o4BPfd6e55bBCAXCHMAJiwWS6qlvU9vtXTr/qe2yDRTg+zfe3ST2jqDamnvUzyRzHczMYLTjps7WJ17epuSSapzQLEjzAGYFNM09fdX9igYjkuSTjiiQXVV3jy3Cgfi87j0oRNT1bldHQN6+pXdeW4RgKkizAGYlNd3dGtneuP2BbMqdfj82jy3CGOpqfLK5XLI43HprHcflNnia80f31RHb2TIGEi325nn1gKYCMIcgAl7s6U7M97K73PpPUc1yWApkoLmdBjavCM11nHrrl6dfeJBkqRwNKGb17ygl97qUPO2Tr3Z2s21BIoMYQ7AhLzV0q3fPbVVppkKCKcumyOvh0pOMYjFU2MdW9r75HU7dPTCeklSe1dI6/+8RS3tfYrFGOsIFBvCHIBx29MZ1M1rXlQ8kZQh6X3L5qihtiLfzcIkLTtsZub6vdnSo63p5WUAFBfCHIBx6emP6Ee/3aT+UEyS9O6jZml+egN3FCeHw9D7ls2WNz1G7h+v7lFbZzDPrQIwUYQ5AAf0VkuPvrf6eXX0hCVJJy+drcVMeCgJAZ87s2NHPGHq149t1mvbOvPcKgATQZgDMKpEMqkH//q2Vtz9QibIvf8dc3XKsXPy3DLk0tyGgE44okGSFIkl9P/ufp4FhYEiQpgDIGn4Nl3dA1H9cM0mrU9v++RyGvrEmYfryguOltPJraPUHHlwvU5eOlsOw1A8Yepn61/Rhudb8t0sAOPgyncDABQGa5uuvoGonv7nbj33ersS6d0BZtT49C+nHKJZ9X61d4fENl2l6ZA51ZrfWKnf/XmrwtGE7n7iDb22vUv/dvoizaxhogtQqPj1GoAkKZ5I6m//3K2frPunnmluywS5xfNrdNa75isWT7BNVxlYOKdGX7v8napOLyr8wht79fXbn9FDT7+tWDyR59YBGAmVOaDMtXYM6C8v7dLfX92jvmdjGxMAAA7kSURBVGAs83xDbYVOOKKBpUfKTE2VVwfNqtL3P/serXniDT398m5F40n97i9v6+lX9ujMdy7QSUtnq8rvkWmaisUIeEC+EeaAMpM0Te1s69dr27v0wht79VZrz5DXq/xuHbe4QQtmVbITQBmydoqIROM69R1ztXBOtR7fuEPtXSG1d4X068c3a80f39CSg+t0/kmH6NA5Vfw9AfKMMAeUOJfLoT2dIb369j41v92p17Z3ZdaKy3bUwnotmlerygqXnA7+cS5n1k4RkuQwpDPfOV9v7OzWa9u71BeMKZE09crWTr2ytVONdRV637FzdNIxszP7vQKwl61h7vHHH9cdd9yhN954Q263W8cff7yuu+46HXHEEXY2AyhpsXhSLXv79fbuXm1p7dHrO7rV1RcZ8diagEdHLazX0kUzdcTB9eroDmvHnp4Rj0X5cjgMHXFQnQ5fUKu2zpDebOnWjrZ+JZKm2rtCWvv/bdHv/rxVxxwyQ4vn12rh7Cod1FQln4d6AWAH237S7rvvPn3961/X4sWLdcMNNygajerXv/61Pvaxj2nNmjUEOmCcYvGkuvsj6uqLqDcYU3d/RJ29EXX1hdXeHdLOtj7FE+aIX+vzONVU79fsGX41zfCrssItwzAUDMeY2IADMgxDTem/OwfPrdG2Xb168rmdatk7oETS1Ka3OrTprY70sdKcmQEtmFWleQ2VmjPDrzkzA6qr8srF0jZATtkS5np6erRixQo1NTVpzZo1qqxMbQF0zjnn6JxzztF3v/td3X333XY0BSgYyaSpaDyhgVBcA+GY+kMxDYTj6g/FFI4m1B+KZf4bSP+/LxgdMknhQHwepxbNq9WcmQF53Q7VVnoY34ScqPZ7dOjcWs2dGVBrx4A2vbFXW3f1ZrrwTVNq3Tug1r0Dw762JuBRfbVPtVVeVfs9qvK7Vel3q6rCrQqPa/CxzyW30yGX0yGX0+DvLjAKW8Lchg0b1N/fr0996lOZICdJTU1N+tCHPqS1a9eqpaVF8+bNs6M5UxKNJxSOjmP2ljlyZWTYYdmP07+t9g4M7RIb56nGzczhCa1Tmco8yPzP3O+gwT8P+d/Qz7ffazJTZzbNVLtNMzWAP/Vc1p/Tj03TVDLrNdM0lUyfYNg5RvuzBt8jmRx6btNMLeERi5uKJZKKx5OKJ1P/jyWSiqX/LxmKx5MKRWOKJ0zF4knFE2bquHhS8URSiUleB6/HOew5p2HI73OpwudSXaVXM2p8mlHtVaXfrWMWNainL6adbWN3nxqG5DCMA46XK6fjnA6joNuX7+MSCVN79g3IaUjHH96g4w9vUCgSU0dPRJ29YXX1RTQQiqsnGB3yteFYQrv2DWjXvuFBbywep0P+Crf8Pqf8Hpd8XpccRvoaOQw5DCPr/8r82ZChigq3HIYUjSb2O1ZDjrXOZ6QfO7OeG3r+9NcaRupY6/l0Wwwj9WcZWasyGpKR/lP2d9fKqEbWs9m51TCMIefI/M/IOteQ44ccul8bBs+V/XWZdh0gLx8oUO//8rCjcxDIDVfqHti739+rXLFaGKhwp65hETDMXP7LPoobb7xR99xzj37xi1/opJNOGvLa2rVr9bWvfU0//vGPdc4550x3UwAAAEqKLQMX2traJKUqcfuznrOOAQAAwPjZEuZCoZAkyeMZPm3d6/UOOQYAAADjZ0uYq6hIrSAfjQ7v345EIkOOAQAAwPjZEuZmzZolSdqzZ8+w16znrGMAAAAwfraEuaVLl0qSXnzxxWGvWc8dc8wxdjQFAACgpNgS5s444wwFAgHdd9996u/vzzy/Z88ePfLIIzr++OM1f/58O5oCAABQUmxZmkSSfvvb3+qb3/ymFi9erI9+9KOKxWJavXq1urq6dPfdd+vII4+0oxkAAAAlxbYwJ0mPPvqo7rzzziF7s15//fVs5QUAADBJtoY5AAAA5Ba7HQMAABQxwhwAAEARI8wBAAAUMcIcAABAESPMAQAAFDHCHAAAQBEjzAEAABQxwhwAAEARc+W7AeWutbVVP/rRj/T0008rGAzq4IMP1ic+8Qldcskl4/r6bdu26aGHHtLTTz+tnTt3amBgQHPmzNF73/teXXnllWpsbJzmTwBp6tdRklatWqXm5mY1Nzdrx44dcjgcam5unsZWl6/HH39cd9xxx5DdaK677rpx70YTCoX005/+VH/4wx/U3t6uxsZGnXPOObr66qtVUVExza1Htqlcy61bt+q+++7Ta6+9ptdee03d3d266KKL9L3vfc+GliPbVK7jk08+qQ0bNmjT/9/evYY0/f1xAH/bfv2mdvGSVqSV3VaKVioGFd1Mu0ImKaYIWZGQVtSDHiSFRUFCgomUl5CMyozRRcRMQfNBPdBZ3qZgoFGtWlI22byWfv8PZPu3n5bmvnNbvl8QyDlnh3N40/bZdvb91tXh48ePkEqlWLhwISIjI7F3717888/fX+rwDhAWpFarERERAa1WiwMHDsDT0xMVFRV49uwZjh49ipMnT446R2pqKm7fvo1NmzYhICAADg4OqKurQ2FhIWbMmIH8/HwsWbJkAnYzeYmRIwAsX74cM2fOhLe3N9ra2tDR0cFizgzkcjnOnj1ruE90f38/7ty5g2/fvuHevXujvngMDAwgLi4O1dXVCAsLQ1BQEFpaWpCfn4/AwEDk5eVBIpFM0G4mN1OzfPjwIc6cOQNPT094eXnh+fPnLOYswNQc169fD6lUiq1bt2LZsmXQ6XQoLi6GUqnE5s2bkZWVBTs7uwnajYUIZDGnT58WZDKZUFpaatSemJgoeHt7C2/evBl1joaGBkGj0QxrLygoEGQymXDixAmxlku/IEaOgiAIb9++NfwdGxsreHt7i7lMEgRBo9EIAQEBwsaNGwWtVmto//Tpk+Dv7y/ExMSMOodcLhdkMplw8eJFo/a8vDxBJpMJcrlc9HXTcGJk2dHRYXj+fP/+vSCTyYSkpCSzrZmGEyPHFy9eCIODg0ZtP378EKKjowWZTCZUVlaKvm5rwzNzFtLT04PS0lJ4enpi27ZtRn1xcXEYGBhAUVHRqPP4+fnByclpWPvu3bsBAC0tLeIsmEYkVo4AsGDBAnMskX5SXl4OnU6HyMhITJ8+3dA+d+5c7Ny5EzU1NVCpVL+do7CwEABw8OBBo/b9+/fD0dHR0E/mJUaWLi4uIz5/0sQRI8d169YN++RNIpFgx44dACbH6yCLOQt5/fo1ent7sXr16mF9q1atgkQiQUNDw7jn//z5MwBg1qxZ456DRmfuHElc9fX1AAB/f/9hffq23+UlCAKUSiVmz54NDw8Poz6pVAofHx8olUoIPL1idqZmSdbBnDnqXwddXV3HuTrbwWLOQtRqNYChdx//NXXqVLi6uhrGjMfVq1cBAPv27Rv3HDQ6c+dI4tI/uY+Ul75NP2YkGo0G3d3dIz5eP0d3dzc6OztFWC39jqlZknUwV45qtRr379+Hk5MTQkJCTFukDfj7f+JhZl1dXcjJyRnz+O3bt8PHxwc9PT0AgH///XfEcVKpFL29veNaU1ZWFsrKyhASEoLw8PBxzTHZWGOOJL7f5SWVSo3GjESf5a/y1rczc/MzNUuyDubIsaurCwkJCdDpdMjIyICzs7PpC7VyLOZM1N3djaysrDGPX7hwIXx8fAyXL+jv7x9xXF9fH1xcXP54Pbdu3UJaWhrWrFmD1NTUv/8XPCKxthzJPH6XV19fn9GYkdjb2//y8T+368eR+ZiaJVkHsXPs6upCfHw8mpubce7cOYSGhoqzUCvHYs5E7u7u4zpcqf/4eKSv4L5//46Ojg54e3v/0Zw3b95ESkoK1q5di8zMTD6R/QFrypHMZ86cOQCG8vrvJXv0GerHjMTZ2RkODg6//OpcrVbD0dGRh+ongKlZknUQM0edTocjR46gtrYWycnJiI6OFnexVoxn5ixEJpPB3t4edXV1w/rq6+sxMDCAlStXjnm+GzduICUlBRs2bEB2djYLuQkido5kXvosamtrh/Xp2/z8/H75eDs7O/j6+qK9vR0fPnww6uvv70dzczN8fX35ifgEMDVLsg5i5ajVanH48GHU1dXh0qVLk6qQA1jMWYyDgwNCQ0OhUqlQVlZm1Ke/6Kj+8iJ67969Q2tr67C5srOzkZqaii1btuD69euGcwZkfmLmSOYXEhKCadOmQS6XQ6fTGdrVajVKSkoQGBiI+fPnAxg6p9Pa2or29najOcLCwgAMfRL+s4KCAnR3d2PPnj1m3gUB4mRJlidGjlqtFocOHUJjYyMuX76MiIiICd2DNZCcP3/+vKUXMVn5+vqiuLgYT548QU9PD1QqFTIyMlBeXo74+Hjs2rXLaHx4eDiysrJw/PhxQ9vdu3eRkpICNzc3xMbGoq2tDS0tLYZ/KpUKixcvnuitTSpi5AgAjx8/RkVFBRQKBaqqqqDVajFlyhQoFAooFAoEBQVN5Lb+Svb29nB2dkZRUREqKysxODiIly9fIjk5GT09PUhPT4e7uzsA4NWrV4iIiIBGozH6NdyKFStQVVWFp0+fQqVSQaPR4NGjR8jMzERgYCCSkpIwZQrfJ5ubGFlqtVrk5uZCoVCgpqYGtbW1sLOzw9evX6FQKKDVarFo0SJLbXFSECPHmJgYNDY2Ijg4GEuXLjV6DdQfn3Fzc7PI/iYKz8xZ0Lx581BQUIC0tDTDu3ovLy9cuHABUVFRY5qjsbERAPDlyxckJSUN6/fw8JgUP8u2JDFyBIAHDx6gurraqC09Pd3wd0JCgmhrnsyioqLg5OSE3NxcXLlyxXAfyFOnTo3pPpASiQQ5OTm4du0aSkpKUFxcDHd3d8TFxSExMZG38ppApmbZ2dlp9H8MAJqamtDU1ARg6I1XcHCwWdZO/2dqjkqlEsDQBYjLy8uH9R87dmzM9122Vbw3KxEREZEN43cBRERERDaMxRwRERGRDWMxR0RERGTDWMwRERER2TAWc0REREQ2jMUcERERkQ1jMUdERERkw1jMEREREdkwFnNERERENozFHBEREZENYzFHREREZMNYzBERERHZMBZzRERERDaMxRwRERGRDWMxR0RERGTD/gfyDK7lA8MVPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YP2jJ87o0vrJAkrSbQIgB8Pni/16amw8RCkexSCSs5ub40lJ5eQVpec+M7GUMALkseUHqKeNSs89oX44dV6R9h9q0e3+rTNMcse5p5Aav16f8/GK1t7fo0KE6ORwuGYYh/tl0Z/0cjdS2eX0xzfh7xmLxsJ6fXyyPJ/VjkHtDhRAAUswaP5jqBal7M2V8vEu6LRhRfVNgRN8L2c8wDBUVlaqsbIx8vgI5HA7CYC9cLqdcrpH92e2NYUgOh0M+X4HKysaoqKg0bX/kUSEEgBQKR2LaM0ILUvfm2PFdYxR3fdSisWV97wIFSPFQ6PXmyevNy3RTRq2yzp+jxsb2DLckfagQAkAK7T3Qqkg03s00khNKLJPGFMrpiFcQdn/UepSzAaB3BEIAGAa32ymPx5X43+4D/sRzJ06pSBx3uRxyOFLf9eN2OTRxTKEkZhoDGDq6jAFgGAzD0I66JoXD8W3q3theL0kqyHOrvrFdBzvH9Y0pz9dIrSd27Phi7dnfqj0HWhWNxeR08Lc+gMEhEALAMIXDMdXWx7tr9x6I/39FsVd1B7uqheUjsFuJ5dhxRfpfSaFwTB8dak9UDAFgoPgzEgBSpC0YVnswvlxEZWn6BuwfObEEAAaLQAgAKWLtTiJJVSNYETzS+Mp8edzx2/mu/UwsATB4BEIASJHWtlDicUmhJ23v63Q4dMzY+ALYVAgBDAWBEABSpKU9LCk+89c7wgtSH8nqNq6t9ysciaX1vQFkPwIhAKRIa3u8QliU7x7x3QVKirxyuRyJZW2Om1QqSYrGTO1vDHRbCsed5nAKIPswyxgAUqS1s0JYlD/y3cVOh6Hte5vUEYpPYonFuvZcffXtj9QRjkqS3G6Hjq8uHfH2AMhuBEIASIFozFRbIB4Ii/PdaXnPcKRruRvTNOVxOxQKx7SztknjyuOznCeOKUpLWwBkN7qMASAF2gJhWTW6wjRUCI9kGIYqiuMzmw+1BNP+/gCyG4EQAFLAGj8opa9CeKTKzqVumv0hJpYAGBQCIQCkgDXDWErPGMLeVCStfdjY2tHPmQDQHYEQAFLAqhA6HYbyvJmZ1VtS4E08bm4jEAIYOAIhAKRA1wzjkV9ypi9F+W45Ot+62R/q/2QASEIgBIAUSOeSM31xOAwVF8Tfv7mNQAhg4AiEADBMsZgpf9Ki1JlUYgVCKoQABoFACADD1NIekrUudHEGK4SSVFIYH0foD4QViTLTGMDAEAgBYJiSZ/QWFWS4QljYFUjpNgYwUARCABimboEw0xXCgqRASLcxgAEiEALAMDV27gziMKR8X2Z3BC3uFghZegbAwBAIAWCYrAphYb5HjgwtOWNxOR2JiS10GQMYKAIhAAyTFQgzPcPYwkxjAINFIASAYTBNMxEIMz3D2GJNLGlpDykaY6YxgKMjEALAMDT5OxLLu4yeCmF86RnTlBpbGEcI4OiGPPp57dq1qqmpUU1Njfbu3SuHw6Gamppez/3rX/+qL33pS70+N3PmTD3xxBMDft9t27bpzjvv1BtvvKFwOKzp06fr8ssv12c+85khfR0AMBwHGtoTj0dNIExaeuZQczCDLQGQLYYcCG+77TYVFxfrxBNPVHt7uxoaGo76mosvvljz5s3rdqy0tHTA77lt2zZdcskl8ng8uvTSS1VeXq6nnnpKq1at0i233KLPf/7zg/46AGA4DjQGEo8zveSMJXnpmcPNgX7OBIC4IQfCjRs3avLkyZKklStXDigQzp07V8uWLRvqW+o///M/FQgE9Otf/1qzZs2SJF144YX6whe+oFtvvVVLlixRcXHxkK8PAINlVQgNQyrIGx0VQo/bqTyvS4GOCBVCAAMy5DGEVhgcrEAgoI6OwY9pqa2t1euvv66Pf/zjiTAoSS6XSytXrpTf79cf//jHIbUJAIbKCoQFPrecjswuOZOstLPbmEAIYCDSOqnkv/7rvzR37lzNnj1bixYt0r333qtwODyg17711luSpFNOOaXHc9Yx6xwASJcDjfFAOFrGD1qsbuPDzUHFTDPDrQEw2qVlSX2Xy6WFCxfqjDPO0Lhx43Tw4EE99dRTuuuuu7R161b95Cc/kcPRfzbdv3+/JGns2LE9nrOOWeekQllZfsqulY2czvjnYffvQ7bi80sP0zRV3zmGsLzYp4J+xhC63U653Y5+z0k+z+isNvZ1/tGuV1Wer217mxSJxhQIRzWGfwtpxc9gdrPj55eWQDhv3rwek0kuuugifetb39Kzzz6rP/zhD1q6dGm/1wgE4jddj6fnzc/j8cgwDAWDdI0ASJ/W9rDagxFJ3Wf2jgZlRd7E49p6P4EQQL8ytummYRi65ppr9Oyzz+rll18+aiDMy8uTJIVCPVfeD4VCMk1TPp8vZe1rbGw/+kk5zPqryO7fh2zF55ceO+uaE4+9bqfa2vveGSQcjiocjvV7TvJ5ZizezdvX+Ue7ntfV1euy88MmHTumsN/3RWrxM5jdsunzq6oqSsl1MrowdXV1tSQNaIbyuHHjJEkHDhzo8Zx1zDoHANLB2qFEkgp8Gfv7ulc+j1Med/wWX3eoLcOtATDaZTQQ7tmzR5JUWVl51HOtmcV/+9vfejy3ZcsWSdLs2bNT2DoA6F9roGtSnM/jzGBLejIMI7Fjyb6DBEIA/UtLIGxsbOxxLBKJ6I477pAkLV68uNtz9fX12rlzZ2LcoCRNmjRJp5xyijZv3qx33nmn23UefPBBFRQU9LgOAIyk1qTuWq9ndFUIpa6lZ/Yd8stkpjGAfgz5DrZ+/Xrt27dPklRXVyfTNHXvvfcmnr/66qsTjy+//HJVVVXp5JNP1tixY3Xw4EH9/ve/144dO/S5z32uR5C7/fbb9eSTT+rXv/61FixYkDj+3e9+VytWrNBll12mL3/5yyorK9PTTz+tt99+WzfffLNKSkqG+uUAwKC1tscrhF63c1StQWixJrq0BSNqaQuppNB7lFcAsKshB8J169Zp8+bN3Y7dddddicfJgXDJkiV68cUX9dBDD6mlpUU+n0/Tp0/XLbfcogsvvFCGMbAb6cyZM/XII4/ojjvu0C9+8YvEXsZ33XWXPvvZzw71SwGAIbEqhPmjbPygpThpSZqDzUECIYA+GSb9CL06eLA1003IqGyaYYWe+PzSY/Ujf9Pf9zSquqpAi+dN7Pfc2cdXqak1pL37mwd03uGW+JCZvmYRD+R6ja0devrV3ZKkq5bN1CdO7LmOK0YGP4PZLZs+v5yYZQwA2SxRIfSOrl1KLMkznw+3sE4rgL4RCAFgiKwxhKO1y9jtciSWnmloHvwe8gDsg0AIAENgmqb8gdEdCA3DUHHnnsYNrVQIAfSNQAgAQ9DeEVG0czeR0RoIJamkMxDSZQygPwRCABgCq7tYGr1jCCWpqHOmcUMLXcYA+kYgBIAhSF6UerRtW5fMqhD6A2F1hKMZbg2A0YpACABD0NKWVCEcxYGwuKBr7cEGuo0B9IFACABD0BroqhDm+0Zvl7E1qUSi2xhA3wiEADAE3cYQjuoKYVcgZGIJgL4QCAFgCKwxhD6PUy7n6L2VFuW7ZW0OSpcxgL6M3rsYAIxi/s4KYVHSfsGjkcvpSOxhTJcxgL4QCAFgCKwKYXH+6B0/aKko8UmiyxhA3wiEADAE1hjCooLRXSGUpIrieCCkyxhAXwiEADAErYHs6DKWuiqEDa0dMk0zw60BMBoRCAFgkEzTVEtbvMu4JBsqhJ2BMByJJYIsACQjEALAIAU6ool9jLOpQijRbQygdwRCABik5EWpiwqyYFJJcVcgPNzMTGMAPREIAWCQkhelLs62CmErFUIAPREIAWCQrCVnpOzoMi7O9yQWz6bLGEBvCIQAMEjdKoRZMKnEMAxVFMcXpz7M4tQAekEgBIBB6l4hHP1jCCWpnLUIAfSDQAgAg2RVCD0uh3weV4ZbMzDlxdb2dQRCAD0RCAFgkKwKYbZUB6WumcbN/pAi0ViGWwNgtCEQAsAgJbaty4IJJRary9iU1NjKOEIA3REIAWCQsjMQehOP6TYGcCQCIQAMkrUwdTZ2GUtSAzONARyBQAgAg2CaZlKFMHsCYXlR0m4lVAgBHIFACACD0BGOKhyJT8rIpi5jr8epwrx4gKXLGMCRCIQAMAjJi1IX5WVPhVCSyotYnBpA7wiEADAILVm2bV2yxOLU7GcM4AgEQgAYhG4VwiwaQyh1TSyhyxjAkQiEADAI2bhtnaW8JN5lHOiIqj0YyXBrAIwmBEIAGAR/twphlnUZJ800ptsYQDICIQAMgtVl7HI65PM4M9yawSkt7Aqwzf5QP2cCsJsh78q+du1a1dTUqKamRnv37pXD4VBNTU2v527evFnPPfecXnvtNe3bt0+GYWjSpEk699xzdckll8jn8/X6uiPdcMMNevLJJ3t97nvf+55WrFgx1C8HAAYkeR9jwzAy3JrBKS3q2q2kyc9MYwBdhhwIb7vtNhUXF+vEE09Ue3u7Ghoa+jz3Rz/6kerq6rR48WJdcsklikQieuGFF3Trrbfq6aef1iOPPCKv19vn64/0wx/+sMex2bNnD+nrAIDBaA1k36LUltICAiGA3g05EG7cuFGTJ0+WJK1cubLfQHjddddp3rx5cru7bqArV67U9ddfr2eeeUaPP/64/vmf/3nA771s2bKhNhsAhqWlzaoQZtf4QSm+OHWe16lAR1RNdBkDSDLkMYRWGByIU089tVsYtCxdulSStH379kG9t2ma8vv9ikajg3odAAxXNm5bl6yks0rYTIUQQJIhVwhT4cCBA5KkioqKQb1u/vz58vv9crlc+tjHPqZrrrlGp512WkrbVlaWn9LrZRunM/63gt2/D9mKz2/k+Du7jKvK8lVWli/DkLxelwqOUjF0u51yux0DPs9wxMcn9nX+QK/n9bpUWOiRacbPqyzL0/6GdvmDEf59jCB+BrObHT+/jAVCv9+vn//853K73TrvvPMG9JqKigqtXLlSs2bNUkFBgXbs2KEHHnhAl156qVavXq1zzz13hFsNwM46QlF1hOM9E8UF2ddlLHVtX8fi1ACSZSQQRiIRfetb31JdXZ1uvPFGHXvssQN63Xe+851u/33mmWdq+fLlOvfcc3XLLbforLPOGvCM5aNpbGxPyXWylfVXkd2/D9mKz29kHGoOJB67HYYaG9vl8bjU0RFRW3v/Y/LC4ajC4diAzzNjpiT1ef5Ar9dR6JXfH1IoFF+IOt8Tv+03tHSooaEt62ZKZwt+BrNbNn1+VVVFKblO2tchjEQiuv766/XKK6/o8ssv15e//OVhXW/s2LG64IIL1NTUpK1bt6amkQDQi27b1uVl6RjCzrUII9GY2titBECntFYIw+Gwrr/+ej333HO68sordd1116XkuhMnTpSkfmc6A8BwWeMHJakwSyaVlBR55XI5ZN3uK0vzEs+1d0RVXtL136ZpKhxmsh5gR2kLhKFQSNdee61eeOEFff3rX9eqVatSdu3du3dLkqqqqlJ2TQA4UqCjq6KW783onLwBczoMbd/bpI7OLuPk5WbefP+gWjq7nN1uh46vLs1IGwFkXlruaKFQSN/4xjf00ksv6Vvf+pauuuqqfs9vaGhQY2OjxowZo6KieN94e3u7nE5njwWsd+3apccff1wVFRWaM2fOiH0NANCeFAjzsiQQSlI4ElNtfaskqS3QFQj3HmiV2xUfOTRxTGrGIQHITkO+o61fv1779u2TJNXV1ck0Td17772J56+++urE429/+9t66aWXdMopp2j8+PHasGFDt2tNnjxZH/vYxxL//dBDD2nNmjX6wQ9+oOXLl0uS9uzZoyuuuEKLFi3SlClTlJ+fr/fff1/r1q1TKBTS6tWr5fFk56w/ANkhkKWBMFlyu5MDLgB7G/Idbd26ddq8eXO3Y3fddVficXIgfOeddyRJW7Zs0ZYtW3pc6/zzz+8WCHtTWVmp0047Ta+99pp+97vfKRgMqry8XAsXLtRll12mmTNnDvVLAYABsQKhYUg+jzPDrRkat8sht9OhcDTWLeACsLchB8IHH3xwwOe++OKLg7r2qlWreowxrKqq0urVqwd1HQBIpUAwPuEiz+PK6uVa8rxOhdtjCjDLGECntC87AwDZyupizdZjm/sDAAAgAElEQVTuYkueL97+9g5mFAOIIxACwAAFciQQWjOk6TIGYCEQAsAAdQXC7Bw/aMlLCoSmaWa4NQBGAwIhAAxQrlUIozFToUgsw60BMBoQCAFggKwxhNmyKHVfkgMt3cYAJAIhAAxYrlQIrUklktTOTGMAIhACwICYpqlA56zcbA+E+VQIARyBQAgAAxAKxxTrnICRK5NKJAIhgDgCIQAMQPI2b9k+htDtcsjljC+szfZ1ACQCIQAMSC7sY5ysay1CFqcGQCAEgAEJhHIrEFpfA5NKAEgEQgAYkFyrEFozjRlDCEAiEALAgCR3rWb7GEKp+/Z17FYCIPvvagAwAtxupwzDSPx38o4exUVeeTzx26fL5ZDDYfR4/WiXl7RbSZjdSgDbIxACQC8Mw9COuiaFw/GwtPujlsRzew+0qr4xIEkaU54vKXsDocRMYwAEQgDoUzgcU219qyTpYFMgcfxgY3uielhe4stI24aLxakBJGMMIQAMQCgSH0Podjm6dSVnq3y2rwOQhEAIAANgdR27Xblx22S3EgDJcuPOBgAjzJpU4smRQJi8WwmLUwPIjTsbAIwwayau25Xd+xgnSyxOTYUQsD0CIQAMgDWG0OPOndtm8lqEAOwtd+5sADCCuiqEuXPbzCMQAuiUO3c2ABhBoXBujSGUumYatwfZrQSwu9y5swHACDFNU+HEsjO5N4YwGjPVEWZiCWBnBEIAOIpozFSss4CWSxXC5KVn/O3hDLYEQKblzp0NAEZI8l6/7hycVCJJrQECIWBnuXNnA4ARYo0flCRPDnYZS1IbgRCwNQIhABxFONo1vi6nZhn7usJta3sogy0BkGm5c2cDgBHSvUKYO7dNt7NrtxI/FULA1nLnzgYAI6TbGMIcCoSGYSS6jZlUAthb7tzZAGCEhCK5OYZQ6ppYQoUQsDcCIQAchbUGoZRbs4ylroklBELA3nLrzgYAIyB5DGEudRlL3QMhu5UA9uU6+im9W7t2rWpqalRTU6O9e/fK4XCopqamz/MjkYjuv/9+rVu3TnV1dSotLdXixYt17bXXqqysbMDvW1dXp9tvv12vvvqq2tvbNWXKFK1YsUIXXXTRUL8UAOiXNYbQ5TTkMIwMtya1rO3rwpGYAh0RuRy59fUBGJghB8LbbrtNxcXFOvHEE9Xe3q6GhoZ+z7/xxhv11FNPaeHChbrssstUV1enBx54QK+//rp+85vfqLCw8KjvuX//fl188cVqbW3Vv/zLv2jixIl68cUX9b3vfU/79u3TtddeO9QvBwD6FOrsMs618YNS97UIm/whVRZ7M9gaAJky5EC4ceNGTZ48WZK0cuXKfgPhpk2b9NRTT2nRokW67777EsdnzZqla665Rr/4xS/0zW9+86jvefvtt+vgwYO655579JnPfEaSdNFFF+nrX/+61q5dq3/6p3/SlClThvolAUCvrAphro0flLrvVtLYGiQQAjY15LubFQYHYsOGDZKkSy+9tNvxM888U8ccc0zi+f4EAgE999xzmjhxYiIMWr785S8rGo3q6aefHnCbAGCgrFnGubQGoSXP21X1bPKzODVgV2m5u7355ptyOByaO3duj+fmzp2ruro6HT58uN9rvPfeewoGg71eY86cOXI6nXrrrbdS1mYAsIQ7J5W4c7HL2Ne9QgjAnobcZTwY+/fvV1lZmTweT4/nxo0blzinoqKi32skn5/M7XarvLw8cU4qlJXlp+xa2cjpjP+tYPfvQ7bi8xs+w5C8XpcK8j2KxOKBMN8X/+9kbrdTbrejx/EjDfY8o3NyR1/np+p9801TLqehSNRUe0eEfzMpws9gdrPj55eWCmEwGOw1DEpKHA8EAv1ew3q+r+t4vV4Fg/x1CyD1rGVncnFSiWEYKvC5JUkNLR0Zbg2ATElLhdDn8ykU6n1sinU8Ly+v32tYz/d1nY6OjkEtX3M0jY3tKbtWNrL+KrL79yFb8fkNn8fjUkdHRG3tIXWE47OMDcNUW3v3e1A4HFU4HOtx/EiDPc+MxdcE7Ov8VL6v1+OU2qRDjQH+zaQIP4PZLZs+v6qqopRcJy0VwnHjxqmxsbHXMNdfV/CR10g+P1k4HFZDQ8NRrwEAgxWNxRTrDGe5OIZQ6ppp3OinQgjYVVoC4ezZsxWLxfTmm2/2eG7r1q2qrq7ud/ygJE2fPl0+n09bt27t8dybb76paDSq2bNnp6zNACB136UkF2cZS11rETa1drBbCWBTabm7LVu2TJJ0//33dzv+wgsvaM+ePTrvvPO6HW9oaNDOnTvV2tqaOJaXl6ezzjpLtbW1ev7557ud/6tf/UpOp1Nnn332CH0FAOzKWoNQyr1t6yzWTOOOcFTBUPQoZwPIRUMeQ7h+/Xrt27dPUnw7OdM0de+99yaev/rqqxOPTz/9dJ1zzjl65plndNVVV2nx4sWqra3VAw88oKlTp+ryyy/vdu2HHnpIa9as0Q9+8AMtX748cfy6667Tpk2b9K//+q969913NXHiRL3wwgt66aWXdOWVV2rq1KlD/XIAoFehpEDocedql3HyWoQd3XYvAWAPQ/6pX7dunTZv3tzt2F133ZV4nBwIJenWW2/V9OnT9cQTT+imm25SaWmpzjvvPF177bUD2rZOkiZMmKBHH31Ud9xxhx599NHEXsY33XSTLr744qF+KQDQp3Ckq2KWsxXCI7avG19RkMHWAMiEIQfCBx98cFDnu91uXXnllbryyiuPeu6qVau0atWqXp+bNGmSbr/99kG9NwAMVXKXca6PIZTiFUIA9pObdzcASJHkSSW5WiFM3s+4me3rAFvKzbsbAKRI9wphbo4hdLscibBLhRCwJwIhAPQjZIMxhIZhqDAvvlsJgRCwp9y8uwFAilgVQpfTkKNzf+Fc1BUI6TIG7IhACAD9sJadydVdSixWIGymQgjYEoEQAPoR7tzHOFdnGFsK86kQAnaW23c4ABimrgphbt8urQphRziqQEckw60BkG65fYcDgGEK2yQQFuV7Eo+ZWALYT27f4QBgmKwKYa5uW2cp6KwQSqxFCNgRgRAA+mFtXZfzFcKkQEiFELCf3L7DAcAwWTuV2GVSicTEEsCOcvsOBwDDEI3FFI2ZknI/EHrdzsTXSIUQsJ/cvsMBwDB0hJJ3KcntMYSGYai0yCtJam6jQgjYDYEQAPrQEe4KhB537t8uSwvjgbCplQohYDe5f4cDgCHqXiHM/dulVSGkyxiwn9y/wwHAEHWrEOZ4l7EklVmBkC5jwHYIhADQh0CHvbqMyzq7jDtC7FYC2E3u3+EAYIiCoa5Q5M3xhamlri5jiW5jwG4IhADQh+QqWa7vVCJ1dRlLTCwB7IZACAB9CHZ2GTsMQy6nkeHWjLzyYl/icQOBELAVAiEA9CHQ2WXs9ThkGLkfCMvoMgZsi0AIAH2wJpXYobtYknwel/K9LklUCAG7IRACQB+sMYR2mFBiKStmcWrAjgiEANAHa5axrQJh59IzVAgBeyEQAkAfrAqhHdYgtFjjCBsJhICt2OcuBwCDFOzcus5WFcLOQNjaFlIkGstwawCkC4EQAHoRikQVjsQDkR0DoSlmGgN2QiAEgF60Bey1KLWlrKhrLcKmVvY0BuyCQAgAvfAHusKQ14ZjCCWpoTWYwZYASCf73OUAYBDaAuHEY6/HThXCrkDIxBLAPgiEANALv027jAt8Lrld8V8NBELAPgiEANCLbhVCGwVCwzBYegawIQIhAPTCb9NAKEnlViBkljFgGwRCAOiFFQgNQ3I5jQy3Jr1KrUDYQiAE7MKVjje55557tGbNmn7PeeWVVzR27Ng+n3/iiSd044039vrckiVLdPfddw+rjQCQzAqEXrdThmGvQGh1GTf5OxQzTTls9vUDdpSWQHjWWWdp8uTJPY7v27dPd955p2bOnNlvGEx21VVXaerUqd2OVVdXp6SdAGBpSwqEdmPtZxyNmWptD6ukwJPhFgEYaWkJhCeccIJOOOGEHsfvvPNOSdJFF1004GudfvrpWrBgQcraBgC9sSqEdpphbElenLqxNUggBGwgY2MIo9GonnjiCeXn5+ucc84Z1Gvb2toUCrGCPoCR0xa0KoT2G2pdXsxahIDdpKVC2JtXXnlFBw4c0AUXXKDCwsIBv+7qq6+W3++XJE2bNk0rVqzQJZdcYrsxPgBGlt/GXcalhQRCwG4yFggfe+wxSdLFF188oPN9Pp/OPvtsnXbaaaqsrNS+ffv06KOP6qabbtK2bdt08803p7R9ZWX5Kb1etnE641URu38fshWf3/BZYwgL8t0qyO+7y9TtdsrtdvR7zlDOMxxG5/v3fn6q39frdamw0CPT9Ki4JE8Oh6FYzFQgHOPf0RDwM5jd7Pj5ZSQQ1tfX65VXXtH06dM1Z86cAb1m6dKlWrp0abdjF198sVasWKHf/OY3uuCCCwZ8LQDoTzgSVTAUlST5PBn7uzljnI744tSHm4M63Mx+xoAdZORO98QTTygSiQxqMklvXC6XrrrqKl155ZV6+eWXUxoIGxvbU3atbGT9VWT370O24vMbnuakBZkNmWpr73vMcjgcVTgc6/ecoZxnxkxJ6vP8VL9vR6FXfn9IoVB8y77SAo8ONwd14HAb/46GgJ/B7JZNn19VVVFKrpP20dKmaerxxx+X1+vVsmXLhn09a8mZhoaGYV8LACTJH7TnPsbJrMWpGxhDCNhC2gPhpk2b9OGHH+qzn/2siouLh329PXv2SJIqKyuHfS0AkOy7j3GyxOLUrR0yTTPDrQEw0tIeCH/7299K6nvtwUAgoJ07d6q+vr7b8cbGxl7PtXZAWbRoUYpbCsCurCVnJPsGwvLOtQg7wlEFOiJHORtAtkvrGMKGhgZt3LhRU6dO1fz583s956233tKXvvQlnX/++br11lsTx88991zNnz9fM2bMSMwyXr9+vfbt26fLLrtMJ510Urq+DAA5zp9UIfTYZB3CkiKvXC6HrF8LlWV5ief8wahKi7v+2zRNhcPRdDcRwAhKayDcsGGDwuHwkCaTnHPOOdq8ebM2bdokv9+vgoICzZw5UzfccIOWLFkyAq0FYFdtga6KmNdjjwqh02Fo+94mdXROKmlp65qEsnXHQbV0Tkpxux06vro0I20EMHLSGggvvfRSXXrppf2es2DBAm3fvr3H8RtuuGGkmgUA3VhdxoYhuZ32qBBKUjgSU219qySpLdAVCPccaElUSieOSc2MRgCji33udAAwQG2ds4zzvC7b7oKU7+2qF7QHGUMI5DoCIQAcwZplbMdFqS1Op0O+zu5yAiGQ+wiEAHAEa1JJntce4wf7ktdZJWxnljGQ8wiEAHAEawxhno0rhJJU4OsMhFQIgZxHIASAI1izjPO89g6E+Z2BMHldRgC5iUAIAEewApDP5l3G+T63JCkUjikSjWW4NQBGEoEQAJJEojEFQ/FFl21fIWSmMWAbBEIASJIcfOw8y1jqHogDIQIhkMsIhACQJHnbOmYZd339gQ62qgNyGYEQAJIkT6Cw+yzjbhVClp4BchqBEACSJO9jbPcxhF6PU9ZGLUECIZDTCIQAkCS5QuizeSB0GEbXbiUEQiCnEQgBIEkbYwi7saqkQcYQAjmNQAgASfyds4wNQ/K6CYRsXwfYA4EQAJJYFcICn1uGNYDOxhIVQpadAXIagRAAklhjCAvz3BluyeiQ1zmGMNgRVcw0M9waACOFQAgASawKIYEwzqoQmpI6QowjBHIVgRAAklhjCAsIhJJYixCwCwIhACShQthd991KCIRAriIQAkASawwhFcK47hVCuoyBXEUgBIBOkWgsEXqoEMbRZQzYA4EQADolr7VHIIxzOR1yu+K/KgiEQO4iEAJAp+RdSgiEXaylZwLMMgZyFoEQADq1BbsqYIwh7GJ1G1MhBHIXgRAAOjX7OxKPi/M9GWzJ6EIgBHIfgRAAOjW0dAXC8hJvBlsyuhAIgdxHIASATg2tQUnxiRRUCLtYaxFGoqZCYcYRArmIQAgAnawKYXmxV4ZhZLg1o0fy0jP+pIk3AHIHgRAAOjW0xCuE5UV0FycjEAK5j0AIAJ0aWq0KoS/DLRldkrevayMQAjmJQAgAkqKxmJr8XV3G6EKFEMh9BEIAkNTUGpJpxh9TIezO63bKGlJJhRDITQRCAFDXDGNJKi8iECYzDEM+T7xKSIUQyE0EQgCQdLglKRDSZdxDfuc4QgIhkJtcRz8lNWbMmNHnc1u2bFFBQcFRr9HY2Kg777xTL7zwgpqamlRdXa0LLrhAX/nKV+Rype1LAZCDGpMXpaZC2IPP65LUQZcxkKPSmqLmz5+viy66qMdxr/fof437/X6tWLFCu3bt0he/+EXNmDFDr7/+um677Tbt2LFDq1evHokmA7AJaw1Cn8epfB9/YB7JmlhChRDITWm9602aNEnLli0b0mt/8Ytf6P3339cNN9ygSy+9VJL0+c9/XiUlJXrggQe0fPlynXbaaalsLgAbscYQVjChpFdWIGzviCgWMzPcGgCplvYxhKFQSH6/f9Cv27Bhg3w+ny655JJux7/yla8kngeAobLGEJYxfrBX1lqEpik1t4Uy3BoAqZbWQPjcc89p7ty5mjdvnhYsWKB///d/16FDh476ukOHDqmurk4nnniifL7uf72PGzdO48eP11tvvTVSzQZgA4lt6xg/2Ks8T1eHUrO/o58zAWSjtHUZz5o1S0uWLNGxxx6rtrY2/eUvf9G6deu0adMmPfbYY6qsrOzztfv375cUD3+9GTdunN57772UtresLD+l18s2Tmf8bwW7fx+yFZ/f4HSEo4mxcdVjClVWli/DkLxelwryPf2+1u12yu12pPw8wxFf+K+v80fqffs6r7ykKygHI1H+bR0FP4PZzY6fX9oC4eOPP97tv5ctW6bZs2fr5ptv1po1a/Qf//Effb42GIx35Xg8vd+oPB5P4hwAGKzDzYHE48qyvAy2ZPTK97kTj5uoEAI5J6NT6b74xS9qzZo1evnll/s9z+omDoV6H7cSCoV6dCUPV2Nje0qvl22sv4rs/n3IVnx+g7Prw6bEY5/DUGNjuzwelzo6Impr73+8XDgcVTgcS/l5ZufEjb7OH6n37es8MxZLPD5wqJ1/W0fBz2B2y6bPr6qqKCXXyejC1IZhaMKECWpoaOj3PKur2Oo6PtL+/fv77E4GgKNpSF6DkFnGvXI5HXK74r8yqBACuSejgTAWi6m2trbf8YOSVFlZqQkTJmjbtm09uob379+vjz76SLNnzx7JpgLIYcnb1pUVMcu4L9bSM81+ZhkDuSYtgbCxsbHX42vXrlVTU5MWLVqUONba2qqdO3f2qBqed955CgQCeuSRR7od/+UvfylJQ17fEACsCmFhnlsetzPDrRm9rKVnqBACuSctYwjvu+8+bd26VQsWLNCECRMUDAb16quv6k9/+pOmTp2qa665JnHuxo0bdeONN+rrX/+6Vq1alTh+xRVX6LnnntPq1atVV1enGTNm6I033tCTTz6pc845h0WpAQwZi1IPjLX0DIEQyD1pCYQLFizQzp07tWHDBjU2NsrhcGjy5Mn62te+pssvv1yFhYVHvUZhYaEefvhh3XnnnfrDH/6gRx99VNXV1bruuusSi1MDwFAk1iBkUep+0WUM5K60BMLFixdr8eLFAzp3+fLlWr58ea/PlZeX6+abb9bNN9+cyuYBsLmGzl1KWJS6f1aXcUc4qkBHJBEQAWQ/fpoB2Irb7ZRhGIn/bg+GFQxFJUlVZXnydHaLulwOORxGr9ewq+QA2OTvIBACOYSfZgC2YhiGdtQ1KRyOr6tX39i1KHUgFFHN7viEtjHl+ZIIhMkKkhanPtwS1PiKggy2BkAqEQgB2E44HFNtfaskqfagP3G8IxRJHE/eqg1xBXldvzKS124EkP0yug4hAGRaeyCSeJy8PRt6KvC5ZfW2H2pmu1AglxAIAdhaWzAsKd45nM+YuH45HIYK8+Kh+TCBEMgpBEIAttYWjFcI87wuJpEMQElBfGmewy0EQiCXEAgB2JpVIcz3UR0ciJJCjyQqhECuIRACsLX2zgphQR7jBweipCAeCBtbOxSNxTLcGgCpQiAEYFumaSa6jAuoEA5IcWG8yzhmmmpqZccSIFcQCAHYVjAUVSxmSuq+xh76ZlUIJcYRArmEQAjAtqzuYokxhANVTCAEchKBEIBtNbd1dXkW5lMhHIhuFUImlgA5g0AIwLaaWuO7bRiGVJoUdNA3j9vZtRYhFUIgZxAIAdhWoz8eCIvzPXI6uR0OVGVpfFs/KoRA7uAOCMC2rAphaZE3wy3JLpUleZKoEAK5hEAIwJZCkWhiyZmyQrqLB6OypKtCaJpmhlsDIBUIhABsKXkNPSqEg2NVCEORmFoD4Qy3BkAqEAgB2JLVXSxJpYUEwsGo6KwQSowjBHIFgRCALVkTSlxOQ0UsOTMoVoVQkhoYRwjkBAIhAFuyKoQlhV4ZhpHh1mQXa5axRIUQyBUEQgC2Y5pmokJYRnfxoBXmueVxx399HKJCCOQEAiEA2/EHwgqFY5Kk0iJmGA+WYRiqKGYtQiCXEAgB2M7BpkDiMRNKhsaaWMJahEBuIBACsJ2DjV2BsIwlZ4akkgohkFMIhABsp76zQujzOJXndWW4NdnJqhC2BSMKhiIZbg2A4SIQArAdq0JId/HQWWMIJaqEQC4gEAKwlVjM1KHmzkDIhJIhK08OhIwjBLIegRCArRxobFckGt9/lyVnhq4yebeSlo5+zgSQDQiEAGzlw3p/4jF7GA9daaFXTkd8QW+6jIHsRyAEYCsfHmhNPGYM4dA5HEZihjZdxkD2IxACsJXag/EKYWGeW24Xt8DhYHFqIHdwNwRgK1aXMd3Fw8fi1EDuIBACsI1QOKoDDe2SpLJCZhgPl1UhbGrtUCQay3BrAAwHgRCAbdQdapMZn2BMhTAFrJnGprpvBwgg+6Rlif7du3fr6aef1quvvqoPP/xQbW1tmjBhgk4//XR99atf1ZgxY456jSeeeEI33nhjr88tWbJEd999d6qbDSDHbN/blHicvGwKhmby2KLE4511LRpfUZDB1gAYjrQEwscff1wPPvigPv3pT+uzn/2s8vLytHXrVj388MN6+umn9fDDD2vatGkDutZVV12lqVOndjtWXV09Es0GkGNq9jRIkkoLPSrKp8t4KEqKvHK5HJJcmjqxRF63Ux3hqHbtb9Gi+ZO6nWuapsLhaGYaCmBQ0hIIlyxZoiuuuEIlJSWJYxdffLHmzp2r73//+7r77rt11113Dehap59+uhYsWDBSTQWQo8KRmN77MF4hnDK+OMOtyV5Oh6Hte5vU0bl/8biKfO3Z36q3Pzismt0NifPcboeOry7NVDMBDFJaxhDOmjWrWxi0nH322ZKk7du3D+p6bW1tCoVCKWkbAHvYWdesUDg+8YFAODzhSEy19a2qrW9VSUG80nqoKagP6poSx8NhJpkA2SQtFcK+HDhwQJJUUVEx4NdcffXV8vvjy0ZMmzZNK1as0CWXXCLDMFLatrKy/JReL9s4nfG/Fez+fchWfH49fbB5b+Lx9GPK1NrW/x+VbrdTbrdDBUfpWh6p84zOXUD6Oj/T7bPOmzS2SG/tPCxJag1ENLkkT5Lk9bpUWOiRadqza56fwexmx88vo4HwzjvvlCRdcMEFRz3X5/Pp7LPP1mmnnabKykrt27dPjz76qG666SZt27ZNN99880g3F0AWe2vHIUnS1AnFKvC5jxoIMTBjy7t+Ye5vaNfkcUX9nA1gtMpYIPzJT36i559/XmeeeabOP//8o56/dOlSLV26tNuxiy++WCtWrNBvfvMbXXDBBZozZ07K2tfY2J6ya2Uj668iu38fshWfX3ftwbB21MbHD554TLk6OiJqa+8/EIbDUYXDsYydZ8bi6+P0dX6m25d8XmmhR03+kOrq/WqbUiZJ6ij0yu8PKdQ51tBu+BnMbtn0+VVVpeaPsIysQ/jAAw/ojjvu0Cc+8Qn96Ec/GnJ3r8vl0lVXXSVJevnll1PZRAA5ZNvepsT6gydPLc9sY3JQVWm8m/hQc0CxziALILukPRD+8pe/1H//93/rtNNO09q1a5WXlzes61lLzjQ0NBzlTAB2Zc1+dTkNTZ9cluHW5B4rEEaiphr9HRluDYChSGsg/NnPfqZbb71Vn/rUp/TTn/502GFQkvbs2SNJqqysHPa1AOSmmt2NkqTjquPr5iG1xpR13csPNrJjCZCN0hYIf/rTn+pHP/qRFi5cqHvvvVdeb+/bRgUCAe3cuVP19fXdjjc2NvZ67po1ayRJixYtSn2jAWS9hpag9nfuX3zSFLqLR0JRvjsRtNnCDshOaZlU8tBDD+n2229XZWWlzjrrLD377LPdni8oKNCZZ54pSXrrrbf0pS99Seeff75uvfXWxDnnnnuu5s+frxkzZiRmGa9fv1779u3TZZddppNOOikdXwqALGNVByUC4UgxDENVZXmqrffrYFMw080BMARpCYRvv/22JOnQoUP6t3/7tx7PV1dXJwJhX8455xxt3rxZmzZtkt/vV0FBgWbOnKkbbrhBS5YsGZF2A8h+1nZ1eV6XprAkyogZU+pTbb1f/kBY7UF7ziwGsllaAuGtt97ardrXnwULFvS6c8kNN9yQ6mYByHGmaSYqhCceUyaHI7UL2KOLNbFEincbM3kHyC4ZWXYGANLhw3q/WjoXoD5pCgFlJFWU+GStIFbPxBIg6xAIAeSs/6s5kHh88tSBb5GJwXM5Haoo9kliYgmQjTK6dR0ApIrb7ey2yH00FtOmd/dLkqZPKtXEMfHxgy6Xg67jEVJVmqdDzUE1tAQVjkQz3RwAg0AgBJATDMPQjromhcMxSdLOumY1++PdxcdNLEksTj2mPF8SgXAkjC3P09/3NCpmSu/XNmvOcVWZbhKAAaLLGEDOCPcfWGEAABY+SURBVIdjqq1vVW19q/7aWR10OgwVF7gTxyPRWIZbmbuqKwvkdsV/rbz9weEMtwbAYBAIAeScUDiqvfV+SdLksYXyuNidJB2cToeO6Vza54O6lsSEHgCjH4EQQM7Zvb9VsZgpSZpWXZLh1tjLtAnFkqSYaer/Oqu0AEY/AiGAnLOzrlmSlO91aVxFfoZbYy9jyvJUmOeWJP35rX0Zbg2AgSIQAsgpLW2hxPZpx04olsNgAkk6GYahYzurhB/sa9FHh9sy3CIAA0EgBJBTdu5rSTyeVl2cwZbYl9VtLCmx9A+A0Y1ACCBnmKapDzq7iytKfCot9Ga4RfZUXODRhMoCSdKmdw4oZpoZbhGAoyEQAsgZ2/c2qS0YkUR1MNOsnWEOtwS148OmDLcGwNEQCAHkhHAkphffqJUkedwOTR1PIMykk6aUydm5I8xf3qHbGBjtCIQAcsJzm/eoyd8hSZp7XKU8btYezKR8n1tzjquUJL2+vV6BjkiGWwSgPwRCAFmvpS2kDX/aJUkqKfBo+qTSDLcIknTG3GpJUqAjqg1/3pXh1gDoD4EQQNZb/6cPEhWo+SeMkcPBUjOjwbwZVTp+Ynxh8D++Xqu6g/4MtwhAXwiEALJabb1fL78ZXwB56oRiVVcVZLhFsBiGoX8+a7oMI75zyUMb35PJjGNgVCIQAshapmnq0Rd3yDQlh2Fo8fxJmW4SjjB5bJEWnTJRkrRtb5M2/70+wy0C0BsCIYCs9cqb+1Szu1GStHj+RFWV5mW4RbCUFHnlcjnk8bh00eLjVVzgkSQ99tL7ipqSx+NK/M/NBCAg4wiEALLSnv2temjjDklSSaFHyz89LcMtQjKnw9D2vU2q2d2gPftbdcacCZKkxtYO/fzpd1Wzu0E1uxu0o65JBtsLAhnnynQDAGCw2oJh/fjJtxWJxuQwDH1t2ckqyvdkulk4QjgSU219qySprMijqlKfDjYFtfnvB1SU79aEygJNHFOU4VYCkKgQAhjl3G5nt+5Fl9up+3/3dx1qDkqSLl58vE6eVimXy8Hs4lHMMAwtOGmsHA5Dpim9vHVfYt1IAJlHhRDAqGYYhnbUNSkcjkmS/vL2R/rbjkOSpOmTSzVlfJFqdjdoTHm+JALhaFZe7NM/zBqnP735UefOMnU6dnxJppsFQARCAFkgHI5p7/4W/W3HIb27q0GSVJTv1inHVybWtisv8WWyiRigY8cXq7U9rK07DskfCOvxl95P7GgCIHPoMgYw6jW2BvXsX/cmwqDLaejTcyewPV2WmjW1XNMmxPearjvUpvvWx8eDAsgcKoQARrVX3/5Iv3imRqHOLuOyIq/OmDNBJYVMIslWhmHo1JPHyR8I60BjQK/9vV7NrR26evksFTM5CMgIKoQARqVAR0Q/f6ZG9z35diIMnnhMmZaeNpkwmAOcDkP/+LFqTaiM7yzzXm2zbnngddWyvR2QEQRCAKPOro9adNOvXtNf3tkvScrzurTolGp9/MQxcjq4beUKr8epFUtm6PRZ4yVJh5qD+q8H39DfdhzMcMsA+6HLGMCoEYnG9PxrH+rJVz5QNBbf83bmseVaPG8iS5TkqIrSPK26cLYmVhXosRffV0coqnvWva0z50/SJWdNlzdpnKhpmgqHoxlsLZC7CIQAMsLtdiZ2qIiZpv767n799qX3Vd8YkBTvUrxw4XFa9qmpqtndQCDMUU6Hofc+bNZxE0t14T9O01N/3qVQJKY/vv6htrx3UMs+eazGVxbI7Xbo+OrSTDcXyFkEQgAZYRiGtn3YoPf2NOmVN/dp/+H2xHPlRV6d+8ljVV1VqPqmgFhfMLdZO5rk+1w6+/Rj9Oe39utgU0ANLUH96tm/d1aJJ0nVmW4pkLsIhADSKhKNqWZ3o/6245A21+xXINTVBejzODV7WoWOn1Qq0zRVW9/K+oI2U5Tv0ZIFk/Turga9ueOQYqb0zgcN+vvuRn1y9gQtnletiVWFmW4mkHMIhABGXH1ju97Z1RD/xb63UR2h7uPAXE5DJ00p18xjy+V2MWnE7hyGoVlTKzShskD/9+4BHW4OKhoz9fLWOr28tU4zJpXq1JljNW/GGBXmuTPdXCAnpDUQPv/88/r5z3+u9957T263W/PmzdO1116rE044YUCvDwQC+vGPf6zf//73qq+v15gxY7R06VJdc801ysvLG+HWAxgIp9Oh2oNteu/DRu2obdaODxt1sCnY4zy3y6FjxxerqtSnSWMKWWQaPVQU+7T01Mmqbwyo9mCbanY3yDSl7R82afuHTfqf59/TnOMq9YmTxmrW1AqVFHolMfkEGIq0BcLf/va3+u53v6vp06fr29/+tkKhkP7nf/5Hl1xyiR555JGjhsJoNKqvfvWr2rx5s5YtW6aPf/zj2r59u+6//369+eab+tWvfiWnk18oQDqZpqnWQFh7D7Tq/dpmvV/XrA/2tSgY6v2XcYHPpWMnlGhadbFOO/n/b+9uY5o62ziA//sCbUFtRQoMYbrNdULQDYl7nrnszeE2WaIzw6HGZbhlJpNt0Q/7MOKii3sh0UyNcSKb0WVTWXBjhCBqgvPDSB5FBRFrNFE3rVLQsXYtLS205/lQ2lBaXoTTAp7/L2la7rfepxdwrp6envsh2J09uGm2RnnWNJHIZDIkJ8Rh0X9m4KbZjv9daoXxRgfszm54vALOX72L81d9l6lJTojDrDQt/puZgmSdGglaNeQynn9KNBxRSQitVitKSkqQkpKCw4cPY9Ik3/kfeXl5yMvLw5YtW3Dw4MFBx6isrMSZM2fw9ttvY+PGjYHy9PR0fPXVV6isrER+fn5Et4NISgRBgLvHC0dXDyx2Fyx2N+5Zu3DP6sRdixP3LE7ctXTBNciRmNgYOZJ0GiRN1SA1MR5TJ6sC3yxW8qNhuk9T4mMx+2EdDOlatHU4cKPVhptmG9w9vguXt3U40NbhQH1zKwBAFaNAamIcUhLioNdpoO/9XZw2RY0p8bFQKvg7SOQXlYSwrq4Odrsda9asCSSDAJCSkoLFixfjyJEjMJlMSEtLG3CMqqoqAMCaNWuCylesWIEdO3agqqqKCSFRH16vgC53D1xuD7q6Pb57tweu3seubg/c3R70eAV0dvXgH5vLl/jZXPjX4YbD2YPu+1xfdppWjcfTdEjUaaCQCZgSHxtIAInEIpfJ8NC0eDw0LR7/zUzGXasTd+524s49Bzr+7YLQ287V7cGNVhtutNrCjjM5LgbaeBV0k2Khm6SCtvdeo1IgVqlAjFKOWKUcMTEKxCjkiI2R95b56mKUciaV9MCISkJ44cIFAEB2dnZIXXZ2No4cOYLm5uYBE0JBENDS0oKkpCRMnx583QGVSoXMzEy0tLRAEIQJs/NxuLrh8QiBn4X+DYTgkv71QkiHUMIQjfpXC32epaf3Sa3/OgefxxCDhtYP3n842zXUGPc7h/DzEODx+m9eeHsvkiwM0EcQBAiC797r9c3B21vm7fvY26ed4HvF+7YTeus9Xi+63N5A0tbt8c3B6+3t4xXgFQCPV4DX60VPjwBXT4+vj9vXp2uU51DJFTKowpyGESOXIz4uBvEaJeLVSkzSxGCyJhaJOjVUMQpkzUqE1daNW22DfxQsk/l27Ar54H+zUm83UPvxMr+xbqeQy5A6LR6p03xL4D2WpsOfrTa03uvEvX+d+NvqhMXmhs3ZHTKmu8eLu1Yn7lqdIXXDJZfJENubOCqVCiiVvp81KqXvWpsCepPK3jZyBRQKme8m993kchmUchlkvT/792P+3ZlSLodSIUOMUgGlwlfvu/kuyCTrfeB/3PcV6r9LDLeP7FsU6B18169MFlo32FgDzKXvfMI/T2hnWb824ca/37HCtff0PvjXFnr+82CvYf/niVHIoFFNjC8+RSUhbGtrA+A7Itifv8zfJhyLxQKHw4FZs2aFrU9JScHZs2dhtVqh04lz4VK9frIo40x0DyXxdaCRmZ8V+vfOdsNvN1zjfTvGot2TTyQPayyioaRI6BJHUTnW7XT63n3FxoYuSK9SqYLahNPV1TVg/77l/nZERERENHxRSQj9l4Rxu90hdS6XK6hNOGq1esD+fcv97YiIiIho+KKSECYn+w7fm83mkDp/mb9NODqdDhqNJmx//xhxcXHQarUizJaIiIhIWqKSEM6dOxcA0NjYGFLnL5szZ86A/WUyGbKystDe3o7bt28H1bndbhiNRmRlZU2YL5QQERERjSdRSQhzc3MRHx+PiooK2O32QLnZbEZtbS1ycnKQnp4OwHcu4bVr19De3h40xtKlSwEA+/fvDyovLy+Hw+HAkiVLIrwVRERERA8mxebNmzdH+knUajV0Oh2qq6tx6tQpeL1enDt3Dps2bYLT6cTOnTuh1+sBAOfPn0d+fj4sFgtyc3MDY8yePRunT5/GsWPHYDKZYLFYUFlZiT179iAnJwfFxcWQy3k9KCIiIqL7FbWl6woKCqDVarFv3z5s3bo1sJbxhg0bhrWWsUKhQFlZGXbv3o3a2lrU1NRAr9ejsLAQRUVFXLaOiIiIaIRkwlBXLyYiIiKiBxo/YyUiIiKSOCaERERERBLHhJCIiIhI4pgQEhEREUkcE0IiIiIiiWNCSERERCRxTAiJiIiIJI4JIREREZHEMSEkIiIikrioLV1H49vt27fxzTffoL6+Hg6HAzNnzsTq1avx1ltvDav/n3/+ierqatTX1+PWrVvo7OxEamoqFixYgLVr1yIpKSnCW0CjjSEAlJWVwWg0wmg04ubNm5DL5TAajRGctbScOHEC33//Pa5evRpYvnP9+vXDWr4TAJxOJ3bv3o2jR4+ivb0dSUlJyMvLQ1FRETQaTYRnT6OJ3/Xr11FRUYHLly/j8uXLsFgsyM/Px5dffhmFmZPfaGJ48uRJ1NXVoampCXfu3IFKpcKMGTOwfPlyvPHGG1AqJ3ZKxaXrCGazGfn5+bDZbHjnnXeQlpaGkydP4vfff8cHH3yA9evXDznGtm3b8OOPP+KFF17AvHnzoNFo0NTUhKqqKkyePBmHDh3CY489FoWtkSYxYggATzzxBKZMmYKMjAxcv34dHR0dTAhFUlFRgY0bN8JgMKCgoAButxs//fQT/vnnHxw+fHjIHZLH40FhYSHOnDmDpUuXYv78+bhy5QoOHTqEnJwcHDhwgGu6R9Bo4/frr7/i008/RVpaGmbOnIk//viDCWGUjTaGzz77LFQqFV5++WU8/vjjsNvtqKmpQUtLC1588UWUlpZCJpNFaWsiQCDJ++STTwSDwSAcP348qLyoqEjIyMgQbty4MeQYzc3NgsViCSkvLy8XDAaD8PHHH4s1XQpDjBgKgiD89ddfgcerV68WMjIyxJymZFksFmHevHnC888/L9hstkB5a2urkJ2dLaxatWrIMSoqKgSDwSBs2bIlqPzAgQOCwWAQKioqRJ83+YgRv46OjsD/yFu3bgkGg0EoLi6O2JwpmBgxrK+vF7xeb1BZT0+PsHLlSsFgMAinTp0Sfd7RxHMIJc7pdOL48eNIS0vDK6+8ElRXWFgIj8eD6urqIceZM2cOtFptSPnrr78OALhy5Yo4E6YQYsUQAB5++OFITFHy6urqYLfbsXz5ckyaNClQnpKSgsWLF+Ps2bMwmUyDjlFVVQUAWLNmTVD5ihUrEBcXF6gn8YkRv6lTp4b9H0nRIUYMFyxYEHIEUKFQ4LXXXgMw8fdzTAgl7urVq+jq6sJTTz0VUvfkk09CoVCgubl5xOO3tbUBAKZNmzbiMWhwkY4hjd6FCxcAANnZ2SF1/rLBYiQIAlpaWpCUlITp06cH1alUKmRmZqKlpQUCzwCKiNHGj8ZeJGPo388lJCSMcHbjAxNCiTObzQB875L6i4mJQUJCQqDNSOzYsQMA8Oabb454DBpcpGNIo+ffYYSLkb/M3yYci8UCh8MRtr9/DIfDAavVKsJsqb/Rxo/GXqRiaDab8fPPP0Or1SI3N3d0kxxjE/srMRTQ2dmJsrKyYbd/9dVXkZmZCafTCQCIjY0N206lUqGrq2tEcyotLcWJEyeQm5uLZcuWjWgMKRmPMSRxDBYjlUoV1CYcf/wGirG/nHGOjNHGj8ZeJGLY2dmJdevWwW63Y9euXdDpdKOf6BhiQviAcDgcKC0tHXb7GTNmIDMzM3CpCrfbHbady+XC1KlT73s+P/zwA7Zv346nn34a27Ztm9jfvIqS8RZDEs9gMXK5XEFtwlGr1QP271vub0fiGm38aOyJHcPOzk6sXbsWRqMRn332GRYtWiTORMcQE8IHhF6vH9EJrf5D5eE+Uuzu7kZHRwcyMjLua8z9+/ejpKQEzzzzDPbs2cN/lMM0nmJI4kpOTgbgi1H/yy/54+ZvE45Op4NGoxnwo3+z2Yy4uDh+aSFCRhs/GntixtBut+P9999HY2MjNm3ahJUrV4o72THCcwglzmAwQK1Wo6mpKaTuwoUL8Hg8mDt37rDH++6771BSUoLnnnsOe/fuZTIYBWLHkMTnf/0bGxtD6vxlc+bMGbC/TCZDVlYW2tvbcfv27aA6t9sNo9GIrKwsHomPkNHGj8aeWDG02Wx477330NTUhC+++OKBSQYBJoSSp9FosGjRIphMJpw4cSKozn+hW/+lY/xu3ryJa9euhYy1d+9ebNu2DS+99BK+/fbbwHkZFFlixpAiIzc3F/Hx8aioqIDdbg+Um81m1NbWIicnB+np6QB85zFdu3YN7e3tQWMsXboUgO8IfF/l5eVwOBxYsmRJhLdCusSIH40tMWJos9nw7rvv4uLFi/j666+Rn58f1W2INMXmzZs3j/UkaGxlZWWhpqYGR48ehdPphMlkwq5du1BXV4e1a9ciLy8vqP2yZctQWlqKjz76KFB28OBBlJSUIDExEatXr8b169dx5cqVwM1kMuHRRx+N9qZJhhgxBIDffvsNJ0+eRENDA06fPg2bzQa5XI6GhgY0NDRg/vz50dysB4ZarYZOp0N1dTVOnToFr9eLc+fOYdOmTXA6ndi5cyf0ej0A4Pz588jPz4fFYgn61uLs2bNx+vRpHDt2DCaTCRaLBZWVldizZw9ycnJQXFwMuZzv8SNBjPjZbDbs27cPDQ0NOHv2LBobGyGTyfD333+joaEBNpsNjzzyyFht4gNPjBiuWrUKFy9exMKFCzFr1qygfZz/dJ/ExMQx2T4x8BxCQmpqKsrLy7F9+/bA0YaZM2fi888/R0FBwbDGuHjxIgDg3r17KC4uDqmfPn36hP9K/ngmRgwB4JdffsGZM2eCynbu3Bl4vG7dOtHmLDUFBQXQarXYt28ftm7dGlhHdcOGDcNaR1WhUKCsrAy7d+9GbW0tampqoNfrUVhYiKKiIi5bF2GjjZ/Vag36WwKAS5cu4dKlSwB8b9IWLlwYkbmTz2hj2NLSAsB3keu6urqQ+g8//HDY65KPR1zLmIiIiEji+PkCERERkcQxISQiIiKSOCaERERERBLHhJCIiIhI4pgQEhEREUkcE0IiIiIiiWNCSERERCRxTAiJiIiIJI4JIREREZHEMSEkIiIikjgmhEREREQSx4SQiIiISOKYEBIRERFJHBNCIiIiIoljQkhEREQkcf8H5+HJLd/8y90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_state_dict = copy.deepcopy(net.model.state_dict())\n",
    "\n",
    "Nsamples = 10\n",
    "\n",
    "for idx, iteration in enumerate(savemodel_its):\n",
    "    \n",
    "    net.model.load_state_dict(save_dicts[idx])\n",
    "    weight_vector = net.get_weight_samples()\n",
    "    fig = plt.figure(dpi=120)\n",
    "    ax = fig.add_subplot(111)\n",
    "    symlim = 1\n",
    "    lim_idxs = np.where(np.logical_and(weight_vector>=symlim, weight_vector<=symlim))\n",
    "    sns.distplot(weight_vector, norm_hist=False, label='it %d' % (iteration), ax=ax)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.set_xlim((-symlim, symlim))\n",
    "\n",
    "\n",
    "net.model.load_state_dict(last_state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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